Data organization and access for mixed media document system

ABSTRACT

A Mixed Media Reality (MMR) system and associated techniques are disclosed. The MMR system provides mechanisms for forming a mixed media document that includes media of at least two types (e.g., printed paper as a first medium and digital content and/or web link as a second medium). In one particular embodiment, the MMR system includes a content-based retrieval database configured with an index table to represent two-dimensional geometric relationships between objects extracted from a printed document in a way that allows look-up using a text-based index. A ranked set of document, page and location hypotheses can be computed given data from the index table. The techniques effectively transform features detected in an image patch into textual terms (or other searchable features) that represent both the features themselves and the geometric relationship between them. A storage facility can be used to store additional characteristics about each document image patch.

RELATED APPLICATIONS

The present application claims priority, under 35 U.S.C. § 119(e), of:U.S. Provisional Patent Application No. 60/710,767, filed on Aug. 23,2005 and entitled “Mixed Document Reality”; U.S. Provisional PatentApplication No. 60/792,912, filed on Apr. 17, 2006 and entitled “Systemsand Method for the Creation of a Mixed Document Environment”; and U.S.Provisional Patent Application No. 60/807,654, filed on Jul. 18, 2006and entitled “Layout-Independent MMR Recognition”, each of which areherein incorporated by reference in their entirety.

FIELD OF THE INVENTION

The invention relates to techniques for producing a mixed media documentthat is formed from at least two media types, and more particularly, toa Mixed Media Reality (MMR) system that uses printed media incombination with electronic media to produce mixed media documents.

BACKGROUND OF THE INVENTION

Document printing and copying technology has been used for many years inmany contexts. By way of example, printers and copiers are used inprivate and commercial office environments, in home environments withpersonal computers, and in document printing and publishing serviceenvironments. However, printing and copying technology has not beenthought of previously as a means to bridge the gap between staticprinted media (i.e., paper documents), and the “virtual world” ofinteractivity that includes the likes of digital communication,networking, information provision, advertising, entertainment, andelectronic commerce.

Printed media has been the primary source of communicating information,such as news and advertising information, for centuries. The advent andever-increasing popularity of personal computers and personal electronicdevices, such as personal digital assistant (PDA) devices and cellulartelephones (e.g., cellular camera phones), over the past few years hasexpanded the concept of printed media by making it available in anelectronically readable and searchable form and by introducinginteractive multimedia capabilities, which are unparalleled bytraditional printed media.

Unfortunately, a gap exists between the virtual multimedia-based worldthat is accessible electronically and the physical world of print media.For example, although almost everyone in the developed world has accessto printed media and to electronic information on a daily basis, usersof printed media and of personal electronic devices do not possess thetools and technology required to form a link between the two (i.e., forfacilitating a mixed media document).

Moreover, there are particular advantageous attributes that conventionalprinted media provides such as tactile feel, no power requirements, andpermanency for organization and storage, which are not provided withvirtual or digital media. Likewise, there are particular advantageousattributes that conventional digital media provides such as portability(e.g., carried in storage of cell phone or laptop) and ease oftransmission (e.g., email).

For these reasons, a need exists for techniques that enable exploitationof the benefits associated with both printed and virtual media.

SUMMARY OF THE INVENTION

At least one aspect of one or more embodiments of the present inventionprovides a computer implemented method for organizing and accessinginformation in a mixed media document system. The method includesgenerating an electronic representation of the paper document,identifying features on the paper document that capture two-dimensionalaspects of the paper document, identifying locations of the features,and indexing the features by their respective locations, therebygenerating an index table. The method may include the preliminary stepof receiving the paper document. The method may include storing one ormore characteristics associated with at least one of the features. Inone such case, the one or more characteristics include one or moreactions including at least one of retrieval of textual information,retrieval of graphical information, executing a process, executing acommand, placing an order, retrieving a video, retrieving a sound,storing information, creating a new document, printing a document, anddisplaying a document. In another particular case, identifying featureson the paper document that capture two-dimensional aspects includesidentifying horizontally aligned (whether adjacent or not) andvertically aligned (whether adjacent or not) objects. In anotherparticular case, identifying features on the paper document that capturetwo-dimensional aspects includes identifying horizontal and verticalword-pairs (whole or partial word-pairs). In another particular case,generating an electronic representation of the paper document is carriedout during a scanning or printing process. In another particular case,identifying features on the paper document that capture two-dimensionalaspects of the paper document includes grouping sequences of text intological lines by checking amount of vertical overlap between twoconsecutive sequences. The method may include receiving one or morequery terms that capture two-dimensional relationships between objectsin a target document, and computing at least one mixed media documentand location hypotheses potentially responsive to the query terms, basedon data from the index table. In one such case, receiving one or morequery terms that capture two-dimensional relationships between objectsin a target document is preceded by receiving the target document,creating an image of at least a patch of target document, and generatingthe one or more query terms based on the image. In one such case,generating the one or more query terms based on the image includesgenerating horizontal and vertical word-pairs extracted from the image.In another particular case, computing at least one mixed media documentand location hypotheses includes locating a stored page that most likelymatches the patch of the target document, and calculating a locationwithin that page that is most likely the center of the patch. In onesuch case, each word-pair is associated with an inverse documentfrequency, and locating a stored page that most likely matches at leasta patch of the target document includes adding the inverse documentfrequency for each word-pair to an accumulator indexed by documentspages on which that word-pair appears. In response to a maximum value inthat accumulator exceeding a threshold, the method continues withoutputting the corresponding document page as a match to the patch. Inone such case, calculating a location within that page that is mostlikely the center of the patch includes adding a weight to each cell ina zone around each word-pair (the weight for each cell can bedetermined, for example, by product of the inverse document frequency ofthat word-pair and a normalized geometric distance between that cell andthe center of the zone), and searching a corresponding Accum array ofthe accumulator for the cell with a maximum value. In response to themaximum value exceeding a threshold, the method further includesreporting coordinates of the cell as the location of the patch. Inanother particular case, reporting computing at least one mixed mediadocument and location hypotheses includes looking-up each of the one ormore query terms in the index table to retrieve one or more locationsassociated with each query term, and for each identified location,identifying one or more candidate regions that contain the location. Inone such case, computing at least one mixed media document and locationhypotheses includes identifying one of the one or more candidate regionsthat is most consistent with all of the one or more query terms. Inresponse to determining that the one of the one or more candidateregions satisfies a pre-defined matching criteria, the method continueswith confirming the region as a match to the target document.

At least one other aspect of one or more embodiments of the presentinvention provide a machine-readable medium (e.g., one or more compactdisks, diskettes, servers, memory sticks, or hard drives, ROMs, RAMs, orany type of media suitable for storing electronic instructions) encodedwith instructions, that when executed by one or more processors, causethe processor to carry out a process for organizing and accessinginformation in a mixed media document system. This process can be, forexample, similar to or a variation of the method described here.

At least one other aspect of one or more embodiments of the presentinvention provide a method for accessing information in a mixed mediadocument system. The method includes receiving one or more query termsthat capture two-dimensional relationships between objects in a targetdocument, and computing at least one mixed media document and locationhypotheses potentially responsive to the query terms, based on data froman index table that indexes document features and feature locations ofmixed media documents. In one particular case, receiving one or morequery terms that capture two-dimensional relationships between objectsin a target document is preceded by receiving the target document,creating an image of at least a patch of target document, and generatingthe one or more query terms based on the image. In one such case,generating the one or more query terms based on the image includesgenerating horizontal and vertical word-pairs extracted from the image.In another such case, computing at least one mixed media document andlocation hypotheses includes locating a stored page that most likelymatches the patch of the target document, and calculating a locationwithin that page that is most likely the center of the patch. In anothersuch case, each word-pair is associated with an inverse documentfrequency, and locating a stored page that most likely matches at leasta patch of the target document includes adding the inverse documentfrequency for each word-pair to an accumulator indexed by documentspages on which that word-pair appears. In response to a maximum value inthat accumulator exceeding a threshold, the method continues withoutputting the corresponding document page as a match to the patch. Inanother such case, calculating a location within that page that is mostlikely the center of the patch includes adding a weight to each cell ina zone around each word-pair (the weight for each cell can bedetermined, for example, by product of the inverse document frequency ofthat word-pair and a normalized geometric distance between that cell andthe center of the zone), and searching a corresponding Accum array ofthe accumulator for the cell with a maximum value. In response to themaximum value exceeding a threshold, the method includes reportingcoordinates of the cell as the location of the patch. In anotherparticular case, computing at least one mixed media document andlocation hypotheses includes looking-up each of the one or more queryterms in the index table to retrieve one or more locations associatedwith each query term, and for each identified location, identifying oneor more candidate regions that contain the location. Computing at leastone mixed media document and location hypotheses may further includeidentifying one of the one or more candidate regions that is mostconsistent with all of the one or more query terms, and in response todetermining that the one of the one or more candidate regions satisfiesa pre-defined matching criteria, confirming the region as a match to thetarget document.

Another embodiment of the present invention provides a machine-readablemedium (e.g., one or more compact disks, diskettes, servers, memorysticks, or hard drives, ROMs, RAMs, or any type of media suitable forstoring electronic instructions) encoded with instructions, that whenexecuted by one or more processors, cause the processor to carry out aprocess for accessing information in a mixed media document system. Thisprocess can be, for example, similar to or a variation of the methoddescribed here.

At least one other aspect of one or more embodiments of the presentinvention provide a machine-readable medium (e.g., one or more compactdisks, diskettes, servers, memory sticks, or hard drives, ROMs, RAMs, orany type of media suitable for storing electronic instructions) encodedwith instructions, that when executed by one or more processors, causethe processor to carry out a process for accessing information in amixed media document system. This process can be, for example, similarto or a variation of the method described here.

The features and advantages described herein are not all-inclusive and,in particular, many additional features and advantages will be apparentto one of ordinary skill in the art in view of the figures anddescription. Moreover, it should be noted that the language used in thespecification has been principally selected for readability andinstructional purposes, and not to limit the scope of the inventivesubject matter.

BRIEF DESCRIPTION OF THE DRAWINGS

The invention is illustrated by way of example, and not by way oflimitation in the figures of the accompanying drawings in which likereference numerals are used to refer to similar elements.

FIG. 1A illustrates a functional block diagram of a Mixed Media Reality(MMR) system configured in accordance with an embodiment of the presentinvention.

FIG. 1B illustrates a functional block diagram of an MMR systemconfigured in accordance with another embodiment of the presentinvention.

FIGS. 2A, 2B, 2C, and 2D illustrate capture devices in accordance withembodiments of the present invention.

FIG. 2E illustrates a functional block diagram of a capture deviceconfigured in accordance with an embodiment of the present invention.

FIG. 3 illustrates a functional block diagram of a MMR computerconfigured in accordance with an embodiment of the present invention.

FIG. 4 illustrates a set of software components included in an MMRsoftware suite configured in accordance with an embodiment of thepresent invention.

FIG. 5 illustrates a diagram representing an embodiment of an MMRdocument configured in accordance with an embodiment of the presentinvention.

FIG. 6 illustrates a document fingerprint matching methodology inaccordance with an embodiment of the present invention.

FIG. 7 illustrates a document fingerprint matching system configured inaccordance with an embodiment of the present invention.

FIG. 8 illustrates a flow process for text/non-text discrimination inaccordance with an embodiment of the present invention.

FIG. 9 illustrates an example of text/non-text discrimination inaccordance with an embodiment of the present invention.

FIG. 10 illustrates a flow process for estimating the point size of textin an image patch in accordance with an embodiment of the presentinvention.

FIG. 11 illustrates a document fingerprint matching technique inaccordance with another embodiment of the present invention.

FIG. 12 illustrates a document fingerprint matching technique inaccordance with another embodiment of the present invention.

FIG. 13 illustrates an example of interactive image analysis inaccordance with an embodiment of the present invention.

FIG. 14 illustrates a document fingerprint matching technique inaccordance with another embodiment of the present invention.

FIG. 15 illustrates an example of word bounding box detection inaccordance with an embodiment of the present invention.

FIG. 16 illustrates a feature extraction technique in accordance with anembodiment of the present invention.

FIG. 17 illustrates a feature extraction technique in accordance withanother embodiment of the present invention.

FIG. 18 illustrates a feature extraction technique in accordance withanother embodiment of the present invention.

FIG. 19 illustrates a feature extraction technique in accordance withanother embodiment of the present invention.

FIG. 20 illustrates a document fingerprint matching technique inaccordance with another embodiment of the present invention.

FIG. 21 illustrates multi-classifier feature extraction for documentfingerprint matching in accordance with an embodiment of the presentinvention.

FIGS. 22 and 23 illustrate an example of a document fingerprint matchingtechnique in accordance with an embodiment of the present invention.

FIG. 24 illustrates a document fingerprint matching technique inaccordance with another embodiment of the present invention.

FIG. 25 illustrates a flow process for database-driven feedback inaccordance with an embodiment of the present invention.

FIG. 26 illustrates a document fingerprint matching technique inaccordance with another embodiment of the present invention.

FIG. 27 illustrates a flow process for database-driven classification inaccordance with an embodiment of the present invention.

FIG. 28 illustrates a document fingerprint matching technique inaccordance with another embodiment of the present invention.

FIG. 29 illustrates a flow process for database-driven multipleclassification in accordance with an embodiment of the presentinvention.

FIG. 30 illustrates a document fingerprint matching technique inaccordance with another embodiment of the present invention.

FIG. 31 illustrates a document fingerprint matching technique inaccordance with another embodiment of the present invention.

FIG. 32 illustrates a document fingerprint matching technique inaccordance with another embodiment of the present invention.

FIG. 33 shows a flow process for multi-tier recognition in accordancewith an embodiment of the present invention.

FIG. 34A illustrates a functional block diagram of an MMR databasesystem configured in accordance with an embodiment of the presentinvention.

FIG. 34B illustrates an example of MMR feature extraction for anOCR-based technique in accordance with an embodiment of the presentinvention.

FIG. 34C illustrates an example index table organization in accordancewith an embodiment of the present invention.

FIG. 35 illustrates a method for generating an MMR index table inaccordance with an embodiment of the present invention.

FIG. 36 illustrates a method for computing a ranked set of document,page, and location hypotheses for a target document, in accordance withan embodiment of the present invention.

FIG. 37A illustrates a functional block diagram of MMR componentsconfigured in accordance with another embodiment of the presentinvention.

FIG. 37B illustrates a set of software components included in MMRprinting software in accordance with an embodiment of the invention.

FIG. 38 illustrates a flowchart of a method of embedding a hot spot in adocument in accordance with an embodiment of the present invention.

FIG. 39A illustrates an example of an HTML file in accordance with anembodiment of the present invention

FIG. 39B illustrates an example of a marked-up version of the HTML fileof FIG. 39A.

FIG. 40A illustrates an example of the HTML file of FIG. 39A displayedin a browser in accordance with an embodiment of the present invention.

FIG. 40B illustrates an example of a printed version of the HTML file ofFIG. 40A, in accordance with an embodiment of the present invention.

FIG. 41 illustrates a symbolic hotspot description in accordance with anembodiment of the present invention.

FIGS. 42A and 42B show an example page_desc.xml file for the HTML fileof FIG. 39A, in accordance with an embodiment of the present invention.

FIG. 43 illustrates a hotspot.xml file corresponding to FIGS. 41, 42A,and 42B, in accordance with an embodiment of the present invention.

FIG. 44 illustrates a flowchart of the process used by a forwarding DLLin accordance with an embodiment of the present invention.

FIG. 45 illustrates a flowchart of a method of transforming characterscorresponding to a hotspot in a document in accordance with anembodiment of the present invention.

FIG. 46 illustrates an example of an electronic version of a documentaccording to an embodiment of the present invention.

FIG. 47 illustrates an example of a printed modified document accordingto an embodiment of the present invention.

FIG. 48 illustrates a flowchart of a method of shared documentannotation in accordance with an embodiment of the present invention.

FIG. 49A illustrates a sample source web page in a browser according toan embodiment of the present invention.

FIG. 49B illustrates a sample modified web page in a browser accordingto an embodiment of the present invention.

FIG. 49C illustrates a sample printed web page according to anembodiment of the present invention.

FIG. 50A illustrates a flowchart of a method of adding a hotspot to animaged document in accordance with an embodiment of the presentinvention.

FIG. 50B illustrates a flowchart of a method of defining a hotspot foraddition to an imaged document in accordance with an embodiment of thepresent invention.

FIG. 51A illustrates an example of a user interface showing a portion ofa newspaper page that has been scanned according to an embodiment.

FIG. 51B illustrates a user interface for defining the data orinteraction to associate with a selected hotspot.

FIG. 51C illustrates the user interface of FIG. 51B including an assignbox in accordance with an embodiment of the present invention.

FIG. 51D illustrates a user interface for displaying hotspots within adocument in accordance with an embodiment of the present invention.

FIG. 52 illustrates a flowchart of a method of using an MMR document andthe MMR system in accordance with an embodiment of the presentinvention.

FIG. 53 illustrates a block diagram of an exemplary set of businessentities associated with the MMR system, in accordance with anembodiment of the present invention.

FIG. 54 illustrates a flowchart of a method, which is a generalizedbusiness method that is facilitated by use of the MMR system, inaccordance with an embodiment of the present invention.

DETAILED DESCRIPTION OF PREFERRED EMBODIMENTS

A Mixed Media Reality (MMR) system and associated methods are described.The MMR system provides mechanisms for forming a mixed media documentthat includes media of at least two types, such as printed paper as afirst medium and a digital photograph, digital movie, digital audiofile, digital text file, or web link as a second medium. The MMR systemand/or techniques can be further used to facilitate various businessmodels that take advantage of the combination of a portable electronicdevice (e.g., a PDA or cellular camera phone) and a paper document toprovide mixed media documents.

In one particular embodiment, the MMR system includes a content-basedretrieval database that represents two-dimensional geometricrelationships between objects extracted from a printed document in a waythat allows look-up using a text-based index. Evidence accumulationtechniques combine the frequency of occurrence of a feature with thelikelihood of its location in a two-dimensional zone. In one suchembodiment, an MMR database system includes an index table that receivesa description computed by an MMR feature extraction algorithm. The indextable identifies the documents, pages, and x-y locations within thosepages where each feature occurs. An evidence accumulation algorithmcomputes a ranked set of document, page and location hypotheses giventhe data from the index table. A relational database (or other suitablestorage facility) can be used to store additional characteristics abouteach document, page, and location, as desired.

The MMR database system may include other components as well, such as anMMR processor, a capture device, a communication mechanism and a memoryincluding MMR software. The MMR processor may also be coupled to astorage or source of media types, an input device and an output device.In one such configuration, the MMR software includes routines executableby the MMR processor for accessing MMR documents with additional digitalcontent, creating or modifying MMR documents, and using a document toperform other operations such business transactions, data queries,reporting, etc.

MMR System Overview

Referring now to FIG. 1A, a Mixed Media Reality (MMR) system 100 a inaccordance with an embodiment of the present invention is shown. The MMRsystem 100 a comprises a MMR processor 102; a communication mechanism104; a capture device 106 having a portable input device 168 and aportable output device 170; a memory including MMR software 108; a basemedia storage 160; an MMR media storage 162; an output device 164; andan input device 166. The MMR system 100 a creates a mixed mediaenvironment by providing a way to use information from an existingprinted document (a first media type) as an index to a second mediatype(s) such as audio, video, text, updated information and services.

The capture device 106 is able to generate a representation of a printeddocument (e.g., an image, drawing, or other such representation), andthe representation is sent to the MMR processor 102. The MMR system 100a then matches the representation to an MMR document and other secondmedia types. The MMR system 100 a is also responsible for taking anaction in response to input and recognition of a representation. Theactions taken by the MMR system 100 a can be any type including, forexample, retrieving information, placing an order, retrieving a video orsound, storing information, creating a new document, printing adocument, displaying a document or image, etc. By use of content-basedretrieval database technology described herein, the MMR system 100 aprovides mechanisms that render printed text into a dynamic medium thatprovides an entry point to electronic content or services of interest orvalue to the user.

The MMR processor 102 processes data signals and may comprise variouscomputing architectures including a complex instruction set computer(CISC) architecture, a reduced instruction set computer (RISC)architecture, or an architecture implementing a combination ofinstruction sets. In one particular embodiment, the MMR processor 102comprises an arithmetic logic unit, a microprocessor, a general purposecomputer, or some other information appliance equipped to perform theoperations of the present invention. In another embodiment, MMRprocessor 102 comprises a general purpose computer having a graphicaluser interface, which may be generated by, for example, a programwritten in Java running on top of an operating system like WINDOWS orUNIX based operating systems. Although only a single processor is shownin FIG. 1A, multiple processors may be included. The processor iscoupled to the MMR memory 108 and executes instructions stored therein.

The communication mechanism 104 is any device or system for coupling thecapture device 106 to the MMR processor 102. For example, thecommunication mechanism 104 can be implemented using a network (e.g.,WAN and/or LAN), a wired link (e.g., USB, RS232, or Ethernet), awireless link (e.g., infrared, Bluetooth, or 802.11), a mobile devicecommunication link (e.g., GPRS or GSM), a public switched telephonenetwork (PSTN) link, or any combination of these. Numerous communicationarchitectures and protocols can be used here.

The capture device 106 includes a means such as a transceiver tointerface with the communication mechanism 104, and is any device thatis capable of capturing an image or data digitally via an input device168. The capture device 106 can optionally include an output device 170and is optionally portable. For example, the capture device 106 is astandard cellular camera phone; a PDA device; a digital camera; abarcode reader; a radio frequency identification (RFID) reader; acomputer peripheral, such as a standard webcam; or a built-in device,such as the video card of a PC. Several examples of capture devices 106a-d are described in more detail with reference to FIGS. 2A-2D,respectively. Additionally, capture device 106 may include a softwareapplication that enables content-based retrieval and that links capturedevice 106 to the infrastructure of MMR system 100 a/100 b. Morefunctional details of capture device 106 are found in reference to FIG.2E. Numerous conventional and customized capture devices 106, and theirrespective functionalities and architectures, will be apparent in lightof this disclosure.

The memory 108 stores instructions and/or data that may be executed byprocessor 102. The instructions and/or data may comprise code forperforming any and/or all of techniques described herein. The memory 108may be a dynamic random access memory (DRAM) device, a static randomaccess memory (SRAM) device, or any other suitable memory device. Thememory 108 is described in more detail below with reference to FIG. 4.In one particular embodiment, the memory 108 includes the MMR softwaresuite, an operating system and other application programs (e.g., wordprocessing applications, electronic mail applications, financialapplications, and web browser applications).

The base media storage 160 is for storing second media types in theiroriginal form, and MMR media storage 162 is for storing MMR documents,databases and other information as detailed herein to create the MMRenvironment. While shown as being separate, in another embodiment, thebase media storage 160 and the MMR media storage 162 may be portions ofthe same storage device or otherwise integrated. The data storage 160,162 further stores data and instructions for MMR processor 102 andcomprises one or more devices including, for example, a hard disk drive,a floppy disk drive, a CD-ROM device, a DVD-ROM device, a DVD-RAMdevice, a DVD-RW device, a flash memory device, or any other suitablemass storage device.

The output device 164 is operatively coupled the MMR processor 102 andrepresents any device equipped to output data such as those thatdisplay, sound, or otherwise present content. For instance, the outputdevice 164 can be any one of a variety of types such as a printer, adisplay device, and/or speakers. Example display output devices 164include a cathode ray tube (CRT), liquid crystal display (LCD), or anyother similarly equipped display device, screen, or monitor. In oneembodiment, the output device 164 is equipped with a touch screen inwhich a touch-sensitive, transparent panel covers the screen of theoutput device 164.

The input device 166 is operatively coupled the MMR processor 102 and isany one of a variety of types such as a keyboard and cursor controller,a scanner, a multifunction printer, a still or video camera, a keypad, atouch screen, a detector, an RFID tag reader, a switch, or any mechanismthat allows a user to interact with system 100 a. In one embodiment theinput device 166 is a keyboard and cursor controller. Cursor control mayinclude, for example, a mouse, a trackball, a stylus, a pen, a touchscreen and/or pad, cursor direction keys, or other mechanisms to causemovement of a cursor. In another embodiment, the input device 166 is amicrophone, audio add-in/expansion card designed for use within ageneral purpose computer system, analog-to-digital converters, anddigital signal processors to facilitate voice recognition and/or audioprocessing.

FIG. 1B illustrates a functional block diagram of an MMR system 100 bconfigured in accordance with another embodiment of the presentinvention. In this embodiment, the MMR system 100 b includes a MMRcomputer 112 (operated by user 110), a networked media server 114, and aprinter 116 that produces a printed document 118. The MMR system 100 bfurther includes an office portal 120, a service provider server 122, anelectronic display 124 that is electrically connected to a set-top box126, and a document scanner 127. A communication link between the MMRcomputer 112, networked media server 114, printer 116, office portal120, service provider server 122, set-top box 126, and document scanner127 is provided via a network 128, which can be a LAN (e.g., office orhome network), WAN (e.g., Internet or corporate network), LAN/WANcombination, or any other data path across which multiple computingdevices may communicate.

The MMR system 100 b further includes a capture device 106 that iscapable of communicating wirelessly to one or more computers 112,networked media server 114, user printer 116, office portal 120, serviceprovider server 122, electronic display 124, set-top box 126, anddocument scanner 127 via a cellular infrastructure 132, wirelessfidelity (Wi-Fi) technology 134, Bluetooth technology 136, and/orinfrared (IR) technology 138. Alternatively, or in addition to, capturedevice 106 is capable of communicating in a wired fashion to MMRcomputer 112, networked media server 114, user printer 116, officeportal 120, service provider server 122, electronic display 124, set-topbox 126, and document scanner 127 via wired technology 140. AlthoughWi-Fi technology 134, Bluetooth technology 136, IR technology 138, andwired technology 140 are shown as separate elements in FIG. 1B, suchtechnology can be integrated into the processing environments (e.g., MMRcomputer 112, networked media server 114, capture device 106, etc) aswell. Additionally, MMR system 100 b further includes a geo locationmechanism 142 that is in wireless or wired communication with theservice provider server 122 or network 128. This could also beintegrated into the capture device 106.

The MMR user 110 is any individual who is using MMR system 100 b. MMRcomputer 112 is any desktop, laptop, networked computer, or other suchprocessing environment. User printer 116 is any home, office, orcommercial printer that can produce printed document 118, which is apaper document that is formed of one or more printed pages.

Networked media server 114 is a networked computer that holdsinformation and/or applications to be accessed by users of MMR system100 b via network 128. In one particular embodiment, networked mediaserver 114 is a centralized computer, upon which is stored a variety ofmedia files, such as text source files, web pages, audio and/or videofiles, image files (e.g., still photos), and the like. Networked mediaserver 114 is, for example, the Comcast Video-on-Demand servers ofComcast Corporation, the Ricoh Document Mall of Ricoh Innovations Inc.,or the Google Image and/or Video servers of Google Inc. Generallystated, networked media server 114 provides access to any data that maybe attached to, integrated with, or otherwise associated with printeddocument 118 via capture device 106.

Office portal 120 is an optional mechanism for capturing events thatoccur in the environment of MMR user 110, such as events that occur inthe office of MMR user 110. Office portal 120 is, for example, acomputer that is separate from MMR computer 112. In this case, officeportal 120 is connected directly to MMR computer 112 or connected to MMRcomputer 112 via network 128. Alternatively, office portal 120 is builtinto MMR computer 112. For example, office portal 120 is constructedfrom a conventional personal computer (PC) and then augmented with theappropriate hardware that supports any associated capture devices 106.Office portal 120 may include capture devices, such as a video cameraand an audio recorder. Additionally, office portal 120 may capture andstore data from MMR computer 112. For example, office portal 120 is ableto receive and monitor functions and events that occur on MMR computer112. As a result, office portal 120 is able to record all audio andvideo in the physical environment of MMR user 110 and record all eventsthat occur on MMR computer 112. In one particular embodiment, officeportal 120 captures events, e.g., a video screen capture while adocument is being edited, from MMR computer 112. In doing so, officeportal 120 captures which websites that were browsed and other documentsthat were consulted while a given document was created. That informationmay be made available later to MMR user 110 through his/her MMR computer112 or capture device 106. Additionally, office portal 120 may be usedas the multimedia server for clips that users add to their documents.Furthermore, office portal 120 may capture other office events, such asconversations (e.g., telephone or in-office) that occur while paperdocuments are on a desktop, discussions on the phone, and small meetingsin the office. A video camera (not shown) on office portal 120 mayidentify paper documents on the physical desktop of MMR user 110, by useof the same content-based retrieval technologies developed for capturedevice 106.

Service provider server 122 is any commercial server that holdsinformation or applications that can be accessed by MMR user 110 of MMRsystem 100 b via network 128. In particular, service provider server 122is representative of any service provider that is associated with MMRsystem 100 b. Service provider server 122 is, for example, but is notlimited to, a commercial server of a cable TV provider, such as ComcastCorporation; a cell phone service provider, such as Verizon Wireless; anInternet service provider, such as Adelphia Communications; an onlinemusic service provider, such as Sony Corporation; and the like.

Electronic display 124 is any display device, such as, but not limitedto, a standard analog or digital television (TV), a flat screen TV, aflat panel display, or a projection system. Set-top box 126 is areceiver device that processes an incoming signal from a satellite dish,aerial, cable, network, or telephone line, as is known. An examplemanufacturer of set-top boxes is Advanced Digital Broadcast. Set-top box126 is electrically connected to the video input of electronic display124.

Document scanner 127 is a commercially available document scannerdevice, such as the KV-S2026C full-color scanner, by PanasonicCorporation. Document scanner 127 is used in the conversion of existingprinted documents into MMR-ready documents. Cellular infrastructure 132is representative of a plurality of cell towers and other cellularnetwork interconnections. In particular, by use of cellularinfrastructure 132, two-way voice and data communications are providedto handheld, portable, and car-mounted phones via wireless modemsincorporated into devices, such as into capture device 106.

Wi-Fi technology 134, Bluetooth technology 136, and IR technology 138are representative of technologies that facilitate wirelesscommunication between electronic devices. Wi-Fi technology 134 istechnology that is associated with wireless local area network (WLAN)products that are based on 802.11 standards, as is known. Bluetoothtechnology 136 is a telecommunications industry specification thatdescribes how cellular phones, computers, and PDAs are interconnected byuse of a short-range wireless connection, as is known. IR technology 138allows electronic devices to communicate via short-range wirelesssignals. For example, IR technology 138 is a line-of-sight wirelesscommunications medium used by television remote controls, laptopcomputers, PDAs, and other devices. IR technology 138 operates in thespectrum from mid-microwave to below visible light. Further, in one ormore other embodiments, wireless communication may be supported usingIEEE 802.15 (UWB) and/or 802.16 (WiMAX) standards.

Wired technology 140 is any wired communications mechanism, such as astandard Ethernet connection or universal serial bus (USB) connection.By use of cellular infrastructure 132, Wi-Fi technology 134, Bluetoothtechnology 136, IR technology 138, and/or wired technology 140, capturedevice 106 is able to communicate bi-directionally with any or allelectronic devices of MMR system 100 b.

Geo-location mechanism 142 is any mechanism suitable for determininggeographic location. Geo-location mechanism 142 is, for example, GPSsatellites which provide position data to terrestrial GPS receiverdevices, as is known. In the example, embodiment shown in FIG. 1B,position data is provided by GPS satellites to users of MMR system 100 bvia service provider server 122 that is connected to network 128 incombination with a GPS receiver (not shown). Alternatively, geo-locationmechanism 142 is a set of cell towers (e.g., a subset of cellularinfrastructure 132) that provide a triangulation mechanism, cell toweridentification (ID) mechanism, and/or enhanced 911 service as a means todetermine geographic location. Alternatively, geo-location mechanism 142is provided by signal strength measurements from known locations of WiFiaccess points or BlueTooth devices.

In operation, capture device 106 serves as a client that is in thepossession of MMR user 110. Software applications exist thereon thatenable a content-based retrieval operation and links capture device 106to the infrastructure of MMR system 100 b via cellular infrastructure132, Wi-Fi technology 134, Bluetooth technology 136, IR technology 138,and/or wired technology 140. Additionally, software applications existon MMR computer 112 that perform several operations, such as but notlimited to, a print capture operation, an event capture operation (e.g.,save the edit history of a document), a server operation (e.g., data andevents saved on MMR computer 112 for later serving to others), or aprinter management operation (e.g., printer 116 may be set up to queuethe data needed for MMR such as document layout and multimedia clips).Networked media server 114 provides access to the data attached to aprinted document, such as printed document 118 that is printed via MMRcomputer 112, belonging to MMR user 110. In doing so, a second medium,such as video or audio, is associated with a first medium, such as apaper document. More details of the software applications and/ormechanisms for forming the association of a second medium to a firstmedium are described in reference to FIGS. 2E, 3, 4, and 5 below.

Capture Device

FIGS. 2A, 2B, 2C, and 2D illustrate example capture devices 106 inaccordance with embodiments of the present invention. More specifically,FIG. 2A shows a capture device 106 a that is a cellular camera phone.FIG. 2B shows a capture device 106 b that is a PDA device. FIG. 2C showsa capture device 106 c that is a computer peripheral device. One exampleof a computer peripheral device is any standard webcam. FIG. 2D shows acapture device 106 d that is built into a computing device (e.g., suchas MMR computer 112). For example, capture device 106 d is a computergraphics card. Example details of capture device 106 are found inreference to FIG. 2E.

In the case of capture devices 106 a and 106 b, the capture device 106may be in the possession of MMR user 110, and the physical locationthereof may be tracked by geo location mechanism 142 or by the IDnumbers of each cell tower within cellular infrastructure 132.

Referring now to FIG. 2E, a functional block diagram for one embodimentof the capture device 106 in accordance with the present invention isshown. The capture device 106 includes a processor 210, a display 212, akeypad 214, a storage device 216, a wireless communications link 218, awired communications link 220, an MMR software suite 222, a capturedevice user interface (UI) 224, a document fingerprint matching module226, a third-party software module 228, and at least one of a variety ofcapture mechanisms 230. Example capture mechanisms 230 include, but arenot limited to, a video camera 232, a still camera 234, a voice recorder236, an electronic highlighter 238, a laser 240, a GPS device 242, andan RFID reader 244.

Processor 210 is a central processing unit (CPU), such as, but notlimited to, the Pentium microprocessor, manufactured by IntelCorporation. Display 212 is any standard video display mechanism, suchthose used in handheld electronic devices. More particularly, display212 is, for example, any digital display, such as a liquid crystaldisplay (LCD) or an organic light-emitting diode (OLED) display. Keypad214 is any standard alphanumeric entry mechanism, such as a keypad thatis used in standard computing devices and handheld electronic devices,such as cellular phones. Storage device 216 is any volatile ornon-volatile memory device, such as a hard disk drive or a random accessmemory (RAM) device, as is well known.

Wireless communications link 218 is a wireless data communicationsmechanism that provides direct point-to-point communication or wirelesscommunication via access points (not shown) and a LAN (e.g., IEEE 802.11Wi-Fi or Bluetooth technology) as is well known. Wired communicationslink 220 is a wired data communications mechanism that provides directcommunication, for example, via standard Ethernet and/or USBconnections.

MMR software suite 222 is the overall management software that performsthe MMR operations, such as merging one type of media with a secondtype. More details of MMR software suite 222 are found with reference toFIG. 4.

Capture device User Interface (UI) 224 is the user interface foroperating capture device 106. By use of capture device UI 224, variousmenus are presented to MMR user 110 for the selection of functionsthereon. More specifically, the menus of capture device UI 224 allow MMRuser 110 to manage tasks, such as, but not limited to, interacting withpaper documents, reading data from existing documents, writing data intoexisting documents, viewing and interacting with the augmented realityassociated with those documents, and viewing and interacting with theaugmented reality associated with documents displayed on his/her MMRcomputer 112.

The document fingerprint matching module 226 is a software module forextracting features from a text image captured via at least one capturemechanism 230 of capture device 106. The document fingerprint matchingmodule 226 can also perform pattern matching between the captured imageand a database of documents. At the most basic level, and in accordancewith one embodiment, the document fingerprint matching module 226determines the position of an image patch within a larger page imagewherein that page image is selected from a large collection ofdocuments. The document fingerprint matching module 226 includesroutines or programs to receive captured data, to extract arepresentation of the image from the captured data, to perform patchrecognition and motion analysis within documents, to perform decisioncombinations, and to output a list of x-y locations within pages wherethe input images are located. For example, the document fingerprintmatching module 226 may be an algorithm that combines horizontal andvertical features that are extracted from an image of a fragment oftext, in order to identify the document and the section within thedocument from which it was extracted. Once the features are extracted, aprinted document index (not shown), which resides, for example, on MMRcomputer 112 or networked media server 114, is queried, in order toidentify the symbolic document. Under the control of capture device UI224, document fingerprint matching module 226 has access to the printeddocument index. The printed document index is described in more detailwith reference to MMR computer 112 of FIG. 3. Note that in an alternateembodiment, the document fingerprint matching module 226 could be partof the MMR computer 112 and not located within the capture device 106.In such an embodiment, the capture device 106 sends raw captured data tothe MMR computer 112 for image extraction, pattern matching, anddocument and position recognition. In yet another embodiment, thedocument fingerprint matching module 226 only performs featureextraction, and the extracted features are sent to the MMR computer 112for pattern matching and recognition.

Third-party software module 228 is representative of any third-partysoftware module for enhancing any operation that may occur on capturedevice 106. Example third-party software includes security software,image sensing software, image processing software, and MMR databasesoftware.

As noted above, the capture device 106 may include any number of capturemechanisms 230, examples of which will now be described.

Video camera 232 is a digital video recording device, such as is foundin standard digital cameras or some cell phones.

Still camera 234 is any standard digital camera device that is capableof capturing digital images.

Voice recorder 236 is any standard audio recording device (microphoneand associated hardware) that is capable of capturing audio signals andoutputting it in digital form.

Electronic highlighter 238 is an electronic highlighter that providesthe ability to scan, store and transfer printed text, barcodes, andsmall images to a PC, laptop computer, or PDA device. Electronichighlighter 238 is, for example, the Quicklink Pen Handheld Scanner, byWizcom Technologies, which allows information to be stored on the pen ortransferred directly to a computer application via a serial port,infrared communications, or USB adapter.

Laser 240 is a light source that produces, through stimulated emission,coherent, near-monochromatic light, as is well known. Laser 240 is, forexample, a standard laser diode, which is a semiconductor device thatemits coherent light when forward biased. Associated with and includedin the laser 240 is a detector that measures the amount of lightreflected by the image at which the laser 240 is directed.

GPS device 242 is any portable GPS receiver device that suppliesposition data, e.g., digital latitude and longitude data. Examples ofportable GPS devices 242 are the NV-U70 Portable Satellite NavigationSystem, from Sony Corporation, and the Magellan brand RoadMate SeriesGPS devices, Meridian Series GPS devices, and eXplorist Series GPSdevices, from Thales North America, Inc. GPS device 242 provides a wayof determining the location of capture device 106, in real time, inpart, by means of triangulation, to a plurality of geo locationmechanisms 142, as is well known.

RFID reader 244 is a commercially available RFID tag reader system, suchas the TI RFID system, manufactured by Texas Instruments. An RFID tag isa wireless device for identifying unique items by use of radio waves. AnRFID tag is formed of a microchip that is attached to an antenna andupon which is stored a unique digital identification number, as is wellknown.

In one particular embodiment, capture device 106 includes processor 210,display 212, keypad, 214, storage device 216, wireless communicationslink 218, wired communications link 220, MMR software suite 222, capturedevice UI 224, document fingerprint matching module 226, third-partysoftware module 228, and at least one of the capture mechanisms 230. Indoing so, capture device 106 is a full-function device. Alternatively,capture device 106 may have lesser functionality and, thus, may includea limited set of functional components. For example, MMR software suite222 and document fingerprint matching module 226 may reside remotely at,for example, MMR computer 112 or networked media server 114 of MMRsystem 100 b and are accessed by capture device 106 via wirelesscommunications link 218 or wired communications link 220.

MMR Computer

Referring now to FIG. 3, the MMR computer 112 configured in accordancewith an embodiment of the present invention is shown. As can be seen,MMR computer 112 is connected to networked media server 114 thatincludes one or more multimedia (MM) files 336, the user printer 116that produces printed document 118, the document scanner 127, and thecapture device 106 that includes capture device UI 224 and a firstinstance of document fingerprint matching module 226. The communicationslink between these components may be a direct link or via a network.Additionally, document scanner 127 includes a second instance ofdocument fingerprint matching module 226′.

The MMR computer 112 of this example embodiment includes one or moresource files 310, a first source document (SD) browser 312, a second SDbrowser 314, a printer driver 316, a printed document (PD) capturemodule 318, a document event database 320 storing a PD index 322, anevent capture module 324, a document parser module 326, a multimedia(MM) clips browser/editor module 328, a printer driver for MM 330, adocument-to-video paper (DVP) printing system 332, and video paperdocument 334.

Source files 310 are representative of any source files that are anelectronic representation of a document (or a portion thereof). Examplesource files 310 include hypertext markup language (HTML) files,Microsoft Word files, Microsoft PowerPoint files, simple text files,portable document format (PDF) files, and the like, that are stored onthe hard drive (or other suitable storage) of MMR computer 112.

The first SD browser 312 and the second SD browser 314 are eitherstand-alone PC applications or plug-ins for existing PC applicationsthat provide access to the data that has been associated with sourcefiles 310. The first and second SD browser 312, 314 may be used toretrieve an original HTML file or MM clips for display on MMR computer112.

Printer driver 316 is printer driver software that controls thecommunication link between applications and the page-descriptionlanguage or printer control language that is used by any particularprinter, as is well known. In particular, whenever a document, such asprinted document 118, is printed, printer driver 316 feeds data that hasthe correct control commands to printer 116, such as those provided byRicoh Corporation for their printing devices. In one embodiment, theprinter driver 316 is different from conventional print drivers in thatit captures automatically a representation of the x-y coordinates, font,and point size of every character on every printed page. In other words,it captures information about the content of every document printed andfeeds back that data to the PD capture module 318.

The PD capture module 318 is a software application that captures theprinted representation of documents, so that the layout of charactersand graphics on the printed pages can be retrieved. Additionally, by useof PD capture module 318, the printed representation of a document iscaptured automatically, in real-time, at the time of printing. Morespecifically, the PD capture module 318 is the software routine thatcaptures the two-dimensional arrangement of text on the printed page andtransmits this information to PD index 322. In one embodiment, the PDcapture module 318 operates by trapping the Windows text layout commandsof every character on the printed page. The text layout commandsindicate to the operating system (OS) the x-y location of everycharacter on the printed page, as well as font, point size, and so on.In essence, PD capture module 318 eavesdrops on the print data that istransmitted to printer 116. In the example shown, the PD capture module318 is coupled to the output of the first SD browser 312 for capture ofdata. Alternatively, the functions of PD capture module 318 may beimplemented directly within printer driver 316. Various configurationswill be apparent in light of this disclosure.

Document event database 320 is any standard database modified to storerelationships between printed documents and events, in accordance withan embodiment of the present invention. (Document event database 320 isfurther described below as MMR database with reference to FIG. 34A.) Forexample, document event database 320 stores bi-directional links fromsource files 310 (e.g., Word, HTML, PDF files) to events that areassociated with printed document 118. Example events include the captureof multimedia clips on capture device 106 immediately after a Worddocument is printed, the addition of multimedia to a document with theclient application of capture device 106, or annotations for multimediaclips. Additionally, other events that are associated with source files310, which may be stored in document event database 320, include loggingwhen a given source file 310 is opened, closed, or removed; logging whena given source file 310 is in an active application on the desktop ofMMR computer 112, logging times and destinations of document “copy” and“move” operations; and logging the edit history of a given source file310. Such events are captured by event capture module 324 and stored indocument event database 320. The document event database 320 is coupledto receive the source files 310, the outputs of the event capture module324, PD capture module 318 and scanner 127, and is also coupled tocapture devices 106 to receive queries and data, and provide output.

The document event database 320 also stores a PD index 322. The PD index322 is a software application that maps features that are extracted fromimages of printed documents onto their symbolic forms (e.g., scannedimage to Word). In one embodiment, the PD capture module 318 provides tothe PD index 322 the x-y location of every character on the printedpage, as well as font, point size, and so on. The PD index 322 isconstructed at the time that a given document is printed. However, allprint data is captured and saved in the PD index 322 in a manner thatcan be interrogated at a later time. For example, if printed document118 contains the word “garden” positioned physically on the page oneline above the word “rose,” the PD index 322 supports such a query(i.e., the word “garden” above the word “rose”). The PD index 322contains a record of which document, which pages, and which locationwithin those pages upon which the word “garden” appears above the word“rose.” Thus, PD index 322 is organized to support a feature-based ortext-based query. The contents of PD index 322, which are electronicrepresentations of printed documents, are generated by use of PD capturemodule 318 during a print operation and/or by use of documentfingerprint matching module 226′ of document scanner 127 during a scanoperation. Additional architecture and functionality of database 320 andPD index 322 will be described below with reference to FIGS. 34A-C, 35,and 36.

The event capture module 324 is a software application that captures onMMR computer 112 events that are associated with a given printeddocument 118 and/or source file 310. These events are captured duringthe lifecycle of a given source file 310 and saved in document eventdatabase 320. In a specific example, by use of event capture module 324,events are captured that relate to an HTML file that is active in abrowser, such as the first SD browser 312, of MMR computer 112. Theseevents might include the time that the HTML file was displayed on MMRcomputer 112 or the file name of other documents that are open at thesame time that the HTML file was displayed or printed. This eventinformation is useful, for example, if MMR user 110 wants to know (at alater time) what documents he/she was viewing or working on at the timethat the HTML file was displayed or printed. Example events that arecaptured by the event capture module 324 include a document edithistory; video from office meetings that occurred near the time when agiven source file 310 was on the desktop (e.g., as captured by officeportal 120); and telephone calls that occurred when a given source file310 was open (e.g., as captured by office portal 120).

Example functions of event capture module 324 include: 1)tracking—tracking active files and applications; 2) key strokecapturing—key stroke capture and association with the activeapplication; 3) frame buffer capturing and indexing—each frame bufferimage is indexed with the optical character recognition (OCR) result ofthe frame buffer data, so that a section of a printed document can bematched to the time it was displayed on the screen. Alternatively, textcan be captured with a graphical display interface (GDI) shadow dll thattraps text drawing commands for the PC desktop that are issued by the PCoperating system. MMR user 110 may point the capture device 106 at adocument and determine when it was active on the desktop of the MMRcomputer 112); and 4) reading history capture—data of the frame buffercapturing and indexing operation is linked with an analysis of the timesat which the documents were active on the desktop of his/her MMRcomputer 112, in order to track how long, and which parts of aparticular document, were visible to MMR user 110. In doing so,correlation may occur with other events, such as keystrokes or mousemovements, in order to infer whether MMR user 110 was reading thedocument.

The combination of document event database 320, PD index 322, and eventcapture module 324 is implemented locally on MMR computer 112 or,alternatively, is implemented as a shared database. If implementedlocally, less security is required, as compared with implementing in ashared fashion.

The document parser module 326 is a software application that parsessource files 310 that are related to respective printed documents 118,to locate useful objects therein, such as uniform resource locators(URLs), addresses, titles, authors, times, or phrases that representlocations, e.g., Hallidie Building. In doing so, the location of thoseobjects in the printed versions of source files 310 is determined. Theoutput of the document parser module 326 can then be used by thereceiving device to augment the presentation of the document 118 withadditional information, and improve the accuracy of pattern matching.Furthermore, the receiving device could also take an action using thelocations, such as in the case of a URL, retrieving the web pagesassociated with the URL. The document parser module 326 is coupled toreceive source files 310 and provides its output to the documentfingerprint matching module 226. Although only shown as being coupled tothe document fingerprint matching module 226 of the capture device, theoutput of document parser module 326 could be coupled to all or anynumber of document fingerprint matching modules 226 wherever they arelocated. Furthermore, the output of the document parser module 326 couldalso be stored in the document event database 320 for later use.

The MM clips browser/editor module 328 is a software application thatprovides an authoring function. The MM clips browser/editor module 328is a standalone software application or, alternatively, a plug-inrunning on a document browser (represented by dashed line to second SDbrowser 314). The MM clips browser/editor module 328 displays multimediafiles to the user and is coupled to the networked media server toreceive multimedia files 336. Additionally, when MMR user 110 isauthoring a document (e.g., attaching multimedia clips to a paperdocument), the MM clips browser/editor module 328 is a support tool forthis function. The MM clips browser/editor module 328 is the applicationthat shows the metadata, such as the information parsed from documentsthat are printed near the time when the multimedia was captured.

The printer driver for MM 330 provides the ability to author MMRdocuments. For example, MMR user 110 may highlight text in a UIgenerated by the printer driver for MM 330 and add actions to the textthat include retrieving multimedia data or executing some other processon network 128 or on MMR computer 112. The combination of printer driverfor MM 330 and DVP printing system 332 provides an alternative outputformat that uses barcodes. This format does not necessarily require acontent-based retrieval technology. The printer driver for MM 330 is aprinter driver for supporting the video paper technology, i.e., videopaper 334. The printer driver for MM 330 creates a paper representationthat includes barcodes as a way to access the multimedia. By contrast,printer driver 316 creates a paper representation that includes MMRtechnology as a way to access the multimedia. The authoring technologyembodied in the combination of MM clips browser/editor 328 and SDbrowser 314 can create the same output format as SD browser 312 thusenabling the creation of MMR documents ready for content-basedretrieval. The DVP printing system 332 performs the linking operation ofany data in document event database 320 that is associated with adocument to its printed representation, either with explicit or implicitbar codes. Implicit bar codes refer to the pattern of text features usedlike a bar code.

Video paper 334 is a technology for presenting audio-visual informationon a printable medium, such as paper. In video paper, bar codes are usedas indices to electronic content stored or accessible in a computer. Theuser scans the bar code and a video clip or other multimedia contentrelated to the text is output by the system. There exist systems forprinting audio or video paper, and these systems in essence provide apaper-based interface for multimedia information.

MM files 336 of the networked media server 114 are representative of acollection of any of a variety of file types and file formats. Forexample, MM files 336 are text source files, web pages, audio files,video files, audio/video files, and image files (e.g., still photos).

As described in FIG. 1B, the document scanner 127 is used in theconversion of existing printed documents into MMR-ready documents.However, with continuing reference to FIG. 3, the document scanner 127is used to MMR-enable existing documents by applying the featureextraction operation of the document fingerprint matching module 226′ toevery page of a document that is scanned. Subsequently, PD index 322 ispopulated with the results of the scanning and feature extractionoperation, and thus, an electronic representation of the scanneddocument is stored in the document event database 320. The informationin the PD index 322 can then be used to author MMR documents.

With continuing reference to FIG. 3, note that the software functions ofMMR computer 112 are not limited to MMR computer 112 only.Alternatively, the software functions shown in FIG. 3 may be distributedin any user-defined configuration between MMR computer 112, networkedmedia server 114, service provider server 122 and capture device 106 ofMMR system 100 b. For example, source files 310, SD browser 312, SDbrowser 314, printer driver 316, PD capture module 318, document eventdatabase 320, PD index 322, event capture module 324, document parsermodule 326, MM clips browser/editor module 328, printer driver for MM330, and DVP printing system 332, may reside fully within capture device106, and thereby, provide enhanced functionality to capture device 106.

MMR Software Suite

FIG. 4 illustrates a set of software components that are included in theMMR software suite 222 in accordance with one embodiment of the presentinvention. It should be understood that all or some of the MMR softwaresuite 222 may be included in the MMR computer 112, the capture device106, the networked media server 114 and other servers. In addition,other embodiments of MMR software suite 222 could have any number of theillustrated components from one to all of them. The MMR software suite222 of this example includes: multimedia annotation software 410 thatincludes a text content-based retrieval component 412, an imagecontent-based retrieval component 414, and a steganographic modificationcomponent 416; a paper reading history log 418; an online readinghistory log 420; a collaborative document review component 422, areal-time notification component 424, a multimedia retrieval component426; a desktop video reminder component 428; a web page remindercomponent 430, a physical history log 432; a completed form reviewercomponent 434; a time transportation component 436, a location awarenesscomponent 438, a PC authoring component 440; a document authoringcomponent 442; a capture device authoring component 444; an unconsciousupload component 446; a document version retrieval component 448; a PCdocument metadata component 450; a capture device UI component 452; anda domain-specific component 454.

The multimedia annotation software 410 in combination with theorganization of document event database 320 form the basic technologiesof MMR system 110 b, in accordance with one particular embodiment. Morespecifically, multimedia annotation software 410 is for managing themultimedia annotation for paper documents. For example, MMR user 110points capture device 106 at any section of a paper document and thenuses at least one capture mechanism 230 of capture device 106 to add anannotation to that section. In a specific example, a lawyer dictatesnotes (create an audio file) about a section of a contract. Themultimedia data (the audio file) is attached automatically to theoriginal electronic version of the document. Subsequent printouts of thedocument optionally include indications of the existence of thoseannotations.

The text content-based retrieval component 412 is a software applicationthat retrieves content-based information from text. For example, by useof text content-based retrieval component 412, content is retrieved froma patch of text, the original document and section within document isidentified, or other information linked to that patch is identified. Thetext content-based retrieval component 412 may utilize OCR-basedtechniques. Alternatively, non-OCR-based techniques for performing thecontent-based retrieval from text operation include the two-dimensionalarrangement of word lengths in a patch of text. One example of textcontent-based retrieval component 412 is an algorithm that combineshorizontal and vertical features that are extracted from an image of afragment of text, to identify the document and the section within thedocument from which it was extracted. The horizontal and verticalfeatures can be used serially, in parallel, or otherwise simultaneously.Such a non-OCR-based feature set is used that provides a high-speedimplementation and robustness in the presence of noise.

The image content-based retrieval component 414 is a softwareapplication that retrieves content-based information from images. Theimage content-based retrieval component 414 performs image comparisonbetween captured data and images in the database 320 to generate a listof possible image matches and associated levels of confidence.Additionally, each image match may have associated data or actions thatare performed in response to user input. In one example, the imagecontent-based retrieval component 414 retrieves content based on, forexample, raster images (e.g., maps) by converting the image to a vectorrepresentation that can be used to query an image database for imageswith the same arrangement of features. Alternative embodiments use thecolor content of an image or the geometric arrangement of objects withinan image to look up matching images in a database.

Steganographic modification component 416 is a software application thatperforms steganographic modifications prior to printing. In order tobetter enable MMR applications, digital information is added to text andimages before they are printed. In an alternate embodiment, thesteganographic modification component 416 generates and stores an MMRdocument that includes: 1) original base content such as text, audio, orvideo information; 2) additional content in any form such as text,audio, video, applets, hypertext links, etc. Steganographicmodifications can include the embedding of a watermark in color orgrayscale images, the printing of a dot pattern on the background of adocument, or the subtle modification of the outline of printedcharacters to encode digital information.

Paper reading history log 418 is the reading history log of paperdocuments. Paper reading history log 418 resides, for example, indocument event database 320. Paper reading history log 418 is based on adocument identification-from-video technology developed by RicohInnovations, which is used to produce a history of the documents read byMMR user 110. Paper reading history log 418 is useful, for example, forreminding MMR user 110 of documents read and/or of any associatedevents.

Online reading history log 420 is the reading history log of onlinedocuments. Online reading history log 420 is based on an analysis ofoperating system events, and resides, for example, in document eventdatabase 320. Online reading history log 420 is a record of the onlinedocuments that were read by MMR user 110 and of which parts of thedocuments were read. Entries in online reading history log 420 may beprinted onto any subsequent printouts in many ways, such as by providinga note at the bottom of each page or by highlighting text with differentcolors that are based on the amount of time spent reading each passage.Additionally, multimedia annotation software 410 may index this data inPD index 322. Optionally, online reading history log 420 may be aided bya MMR computer 112 that is instrumented with devices, such as a facedetection system that monitors MMR computer 112.

The collaborative document review component 422 is a softwareapplication that allows more than one reader of different versions ofthe same paper document to review comments applied by other readers bypointing his/her capture device 106 at any section of the document. Forexample, the annotations may be displayed on capture device 106 asoverlays on top of a document thumbnail. The collaborative documentreview component 422 may be implemented with or otherwise cooperate withany type of existing collaboration software.

The real-time notification component 424 is a software application thatperforms a real-time notification of a document being read. For example,while MMR user 110 reads a document, his/her reading trace is posted ona blog or on an online bulletin board. As a result, other peopleinterested in the same topic may drop-in and chat about the document.

Multimedia retrieval component 426 is a software application thatretrieves multimedia from an arbitrary paper document. For example, MMRuser 110 may retrieve all the conversations that took place while anarbitrary paper document was present on the desk of MMR user 110 bypointing capture device 106 at the document. This assumes the existenceof office portal 120 in the office of MMR user 110 (or other suitablemechanism) that captures multimedia data.

The desktop video reminder component 428 is a software application thatreminds the MMR user 110 of events that occur on MMR computer 112. Forexample, by pointing capture device 106 at a section of a paperdocument, the MMR user 110 may see video clips that show changes in thedesktop of MMR computer 112 that occurred while that section wasvisible. Additionally, the desktop video reminder component 428 may beused to retrieve other multimedia recorded by MMR computer 112, such asaudio that is present in the vicinity of MMR computer 112.

The web page reminder component 430 is a software application thatreminds the MMR user 110 of web pages viewed on his/her MMR computer112. For example, by panning capture device 106 over a paper document,the MMR user 110 may see a trace of the web pages that were viewed whilethe corresponding section of the document was shown on the desktop ofMMR computer 112. The web pages may be shown in a browser, such as SDbrowser 312, 314, or on display 212 of capture device 106.Alternatively, the web pages are presented as raw URLs on display 212 ofcapture device 106 or on the MMR computer 112.

The physical history log 432 resides, for example, in document eventdatabase 320. The physical history log 432 is the physical history logof paper documents. For example, MMR user 110 points his/her capturedevice 106 at a paper document, and by use of information stored inphysical history log 432, other documents that were adjacent to thedocument of interest at some time in the past are determined. Thisoperation is facilitated by, for example, an RFID-like tracking system.In this case, capture device 106 includes an RFID reader 244.

The completed form reviewer component 434 is a software application thatretrieves previously acquired information used for completing a form.For example, MMR user 110 points his/her capture device 106 at a blankform (e.g., a medical claim form printed from a website) and is provideda history of previously entered information. Subsequently, the form isfilled in automatically with this previously entered information by thiscompleted form reviewer component 434.

The time transportation component 436 is a software application thatretrieves source files for past and future versions of a document, andretrieves and displays a list of events that are associated with thoseversions. This operation compensates for the fact that the printeddocument in hand may have been generated from a version of the documentthat was created months after the most significant external events(e.g., discussions or meetings) associated therewith.

The location awareness component 438 is a software application thatmanages location-aware paper documents. The management of location-awarepaper documents is facilitated by, for example, an RFID-like trackingsystem. For example, capture device 106 captures a trace of thegeographic location of MMR user 110 throughout the day and scans theRFID tags attached to documents or folders that contain documents. TheRFID scanning operation is performed by an RFID reader 244 of capturedevice 106, to detect any RFID tags within its range. The geographiclocation of MMR user 110 may be tracked by the identification numbers ofeach cell tower within cellular infrastructure 132 or, alternatively,via a GPS device 242 of capture device 106, in combination with geolocation mechanism 142. Alternatively, document identification may beaccomplished with “always-on video” or a video camera 232 of capturedevice 106. The location data provides “geo-referenced” documents, whichenables a map-based interface that shows, throughout the day, wheredocuments are located. An application would be a lawyer who carriesfiles on visits to remote clients. In an alternate embodiment, thedocument 118 includes a sensing mechanism attached thereto that cansense when the document is moved and perform some rudimentary facedetection operation. The sensing function is via a set of gyroscopes orsimilar device that is attached to paper documents. Based on positioninformation, the MMR system 100 b indicates when to “call” the owner'scellular phone to tell him/her that the document is moving. The cellularphone may add that document to its virtual brief case. Additionally,this is the concept of an “invisible” barcode, which is amachine-readable marking that is visible to a video camera 232 or stillcamera 234 of capture device 106, but that is invisible or very faint tohumans. Various inks and steganography or, a printed-image watermarkingtechnique that may be decoded on capture device 106, may be consideredto determine position.

The PC authoring component 440 is a software application that performsan authoring operation on a PC, such as on MMR computer 112. The PCauthoring component 440 is supplied as plug-ins for existing authoringapplications, such as Microsoft Word, PowerPoint, and web page authoringpackages. The PC authoring component 440 allows MMR user 110 to preparepaper documents that have links to events from his/her MMR computer 112or to events in his/her environment; allows paper documents that havelinks to be generated automatically, such as printed document 118 beinglinked automatically to the Word file from which it was generated; orallows MMR user 110 to retrieve a Word file and give it to someone else.Paper documents that have links are heretofore referred to as MMRdocuments. More details of MMR documents are further described withreference to FIG. 5.

The document authoring component 442 is a software application thatperforms an authoring operation for existing documents. The documentauthoring component 442 can be implemented, for example, either as apersonal edition or as an enterprise edition. In a personal edition, MMRuser 110 scans documents and adds them to an MMR document database(e.g., the document event database 320). In an enterprise edition, apublisher (or a third party) creates MMR documents from the originalelectronic source (or electronic galley proofs). This functionality maybe embedded in high-end publishing packages (e.g., Adobe Reader) andlinked with a backend service provided by another entity.

The capture device authoring component 444 is a software applicationthat performs an authoring operation directly on capture device 106.Using the capture device authoring component 444, the MMR user 110extracts key phrases from the paper documents in his/her hands andstores the key phrases along with additional content captured on-the-flyto create a temporary MMR document. Additionally, by use of capturedevice authoring component 444, the MMR user 110 may return to his/herMMR computer 112 and download the temporary MMR document that he/shecreated into an existing document application, such as PowerPoint, thenedit it to a final version of an MMR document or other standard type ofdocument for another application. In doing so, images and text areinserted automatically in the pages of the existing document, such asinto the pages of a PowerPoint document.

Unconscious upload component 446 is a software application that uploadsunconsciously (automatically, without user intervention) printeddocuments to capture device 106. Because capture device 106 is in thepossession of the MMR user 110 at most times, including when the MMRuser 110 is at his/her MMR computer 112, the printer driver 316 inaddition to sending documents to the printer 116, may also push thosesame documents to a storage device 216 of capture device 106 via awireless communications link 218 of capture device 106, in combinationwith Wi-Fi technology 134 or Bluetooth technology 136, or by wiredconnection if the capture device 106 is coupled to/docked with the MMRcomputer 112. In this way, the MMR user 110 never forgets to pick up adocument after it is printed because it is automatically uploaded to thecapture device 106.

The document version retrieval component 448 is a software applicationthat retrieves past and future versions of a given source file 310. Forexample, the MMR user 110 points capture device 106 at a printeddocument and then the document version retrieval component 448 locatesthe current source file 310 (e.g., a Word file) and other past andfuture versions of source file 310. In one particular embodiment, thisoperation uses Windows file tracking software that keeps track of thelocations to which source files 310 are copied and moved. Other suchfile tracking software can be used here as well. For example, GoogleDesktop Search or the Microsoft Windows Search Companion can find thecurrent version of a file with queries composed from words chosen fromsource file 310.

The PC document metadata component 450 is a software application thatretrieves metadata of a document. For example, the MMR user 110 pointscapture device 106 at a printed document, and the PC document metadatacomponent 450 determines who printed the document, when the document wasprinted, where the document was printed, and the file path for a givensource file 310 at the time of printing.

The capture device UI component 452 is a software application thatmanages the operation of UI of capture device 106, which allows the MMRuser 110 to interact with paper documents. A combination of capturedevice UI component 452 and capture device UI 224 allow the MMR user 110to read data from existing documents and write data into existingdocuments, view and interact with the augmented reality associated withthose documents (i.e., via capture device 106, the MMR user 110 is ableto view what happened when the document was created or while it wasedited), and view and interact with the augmented reality that isassociated with documents displayed on his/her capture device 106.

Domain-specific component 454 is a software application that managesdomain-specific functions. For example, in a music application,domain-specific component 454 is a software application that matches themusic that is detected via, for example, a voice recorder 236 of capturedevice 106, to a title, an artist, or a composer. In this way, items ofinterest, such as sheet music or music CDs related to the detectedmusic, may be presented to the MMR user 110. Similarly, thedomain-specific component 454 is adapted to operate in a similar mannerfor video content, video games, and any entertainment information. Thedevice specific component 454 may also be adapted for electronicversions of any mass media content.

With continuing reference to FIGS. 3 and 4, note that the softwarecomponents of MMR software suite 222 may reside fully or in part on oneor more MMR computers 112, networked servers 114, service providerservers 122, and capture devices 106 of MMR system 100 b. In otherwords, the operations of MMR system 110 b, such as any performed by MMRsoftware suite 222, may be distributed in any user-defined configurationbetween MMR computer 112, networked server 114, service provider server122, and capture device 106 (or other such processing environmentsincluded in the system 100 b).

In will be apparent in light of this disclosure that the basefunctionality of the MMR system 100 a/100 b can be performed withcertain combinations of software components of the MMR software suite222. For example, the base functionality of one embodiment of the MMRsystem 100 a/100 b includes:

-   creating or adding to an MMR document that includes a first media    portion and a second media portion;-   use of the first media portion (e.g., a paper document) of the MMR    document to access information in the second media portion;-   use of the first media portion (e.g., a paper document) of the MMR    document to trigger or initiate a process in the electronic domain;-   use of the first media portion (e.g., a paper document) of the MMR    document to create or add to the second media portion;-   use of the second media portion of the MMR document to create or add    to the first media portion;-   use of the second media portion of the MMR document to trigger or    initiate a process in the electronic domain or related to the first    media portion.

MMR Document

FIG. 5 illustrates a diagram of an MMR document 500 in accordance withone embodiment of the present invention. More specifically, FIG. 5 showsan MMR document 500 including a representation 502 of a portion of theprinted document 118, an action or second media 504, an index or hotspot506, and an electronic representation 508 of the entire document 118.While the MMR document 500 typically is stored at the document eventdatabase 320, it could also be stored in the capture device or any otherdevices coupled to the network 128. In one embodiment, multiple MMRdocuments may correspond to a printed document. In another embodiment,the structure shown in FIG. 5 is replicated to create multiple hotspots506 in a single printed document. In one particular embodiment, the MMRdocument 500 includes the representation 502 and hotspot 506 with pageand location within a page; the second media 504 and the electronicrepresentation 508 are optional and delineated as such by dashed lines.Note that the second media 504 and the electronic representation 508could be added later after the MMR document has been created, if sodesired. This basic embodiment can be used to locate a document orparticular location in a document that correspond to the representation.

The representation 502 of a portion of the printed document 118 can bein any form (images, vectors, pixels, text, codes, etc.) usable forpattern matching and that identifies at least one location in thedocument. It is preferable that the representation 502 uniquely identifya location in the printed document. In one embodiment, therepresentation 502 is a text fingerprint as shown in FIG. 5. The textfingerprint 502 is captured automatically via PD capture module 318 andstored in PD index 322 during a print operation. Alternatively, the textfingerprint 502 is captured automatically via document fingerprintmatching module 226′ of document scanner 127 and stored in PD index 322during a scan operation. The representation 502 could alternatively bethe entire document, a patch of text, a single word if it is a uniqueinstance in the document, a section of an image, a unique attribute orany other representation of a matchable portion of a document.

The action or second media 504 is preferably a digital file or datastructure of any type. The second media 504 in the most basic embodimentmay be text to be presented or one or more commands to be executed. Thesecond media type 504 more typically is a text file, audio file, orvideo file related to the portion of the document identified by therepresentation 502. The second media type 504 could be a data structureor file referencing or including multiple different media types, andmultiple files of the same type. For example, the second media 504 canbe text, a command, an image, a PDF file, a video file, an audio file,an application file (e.g. spreadsheet or word processing document), etc.

The index or hotspot 506 is a link between the representation 502 andthe action or second media 504. The hotspot 506 associates therepresentation 502 and the second media 504. In one embodiment, theindex or hotspot 506 includes position information such as x and ycoordinates within the document. The hotspot 506 maybe a point, an areaor even the entire document. In one embodiment, the hotspot is a datastructure with a pointer to the representation 502, a pointer to thesecond media 504, and a location within the document. It should beunderstood that the MMR document 500 could have multiple hotspots 506,and in such a case the data structure creates links between multiplerepresentations, multiple second media files, and multiple locationswithin the printed document 118.

In an alternate embodiment, the MMR document 500 includes an electronicrepresentation 508 of the entire document 118. This electronicrepresentation can be used in determining position of the hotspot 506and also by the user interface for displaying the document on capturedevice 106 or the MMR computer 112.

Example use of the MMR document 500 is as follows. By analyzing textfingerprint or representation 502, a captured text fragment isidentified via document fingerprint matching module 226 of capturedevice 106. For example, MMR user 110 points a video camera 232 or stillcamera 234 of his/her capture device 106 at printed document 118 andcaptures an image. Subsequently, document fingerprint matching module226 performs its analysis upon the captured image, to determine whetheran associated entry exists within the PD index 322. If a match is found,the existence of a hot spot 506 is highlighted to MMR user 110 on thedisplay 212 of his/her capture device 106. For example, a word or phraseis highlighted, as shown in FIG. 5. Each hot spot 506 within printeddocument 118 serves as a link to other user-defined or predetermineddata, such as one of MM files 336 that reside upon networked mediaserver 114. Access to text fingerprints or representations 502 that arestored in PD index 322 allows electronic data to be added to any MMRdocument 500 or any hotspot 506 within a document. As described withreference to FIG. 4, a paper document that includes at least one hotspot 506 (e.g., link) is referred to as an MMR document 500.

With continuing reference to Figures 1B, 2A through 2D, 3, 4, and 5,example operation of MMR system 100 b is as follows. MMR user 110 or anyother entity, such as a publishing company, opens a given source file310 and initiates a printing operation to produce a paper document, suchas printed document 118. During the printing operation, certain actionsare performed automatically, such as: (1) capturing automatically theprinted format, via PD capture module 318, at the time of printing andtransferring it to capture device 106. The electronic representation 508of a document is captured automatically at the time of printing, by useof PD capture module 318 at the output of, for example, SD browser 312.For example, MMR user 110 prints content from SD browser 312 and thecontent is filtered through PD capture module 318. As previouslydiscussed, the two-dimensional arrangement of text on a page can bedetermined when the document is laid out for printing; (2) capturingautomatically, via PD capture module 318, the given source file 310 atthe time of printing; and (3) parsing, via document parser module 326,the printed format and/or source file 310, in order to locate “namedentities” or other interesting information that may populate amultimedia annotation interface on capture device 106. The namedentities are, for example, “anchors” for adding multimedia later, i.e.,automatically generated hot spots 506. Document parser module 326receives as input source files 310 that are related to a given printeddocument 118. Document parser module 326 is the application thatidentifies representations 502 for use with hot spots 506, such astitles, authors, times, or locations, in a paper document 118 and, thus,prompts information to be received on capture device 106; (4) indexingautomatically the printed format and/or source file 310 forcontent-based retrieval, i.e., building PD index 322; (5) making entriesin document event database 320 for documents and events associated withsource file 310, e.g., edit history and current location; and (6)performing an interactive dialog within printer driver 316, which allowsMMR user 110 to add hot spots 506 to documents before they are printedand, thus, an MMR document 500 is formed. The associated data is storedon MMR computer 112 or uploaded to networked media server 114.

Exemplary Alternate Embodiments

The MMR system 100 (100 a or 100 b) is not limited to the configurationsshown in FIGS. 1A-1B, 2A-2D, and 3-5. The MMR Software may bedistributed in whole or in part between the capture device 106 and theMMR computer 112, and significantly fewer than all the modules describedabove with reference to FIGS. 3 and 4 are required. Multipleconfigurations are possible including the following:

A first alternate embodiment of the MMR system 100 includes the capturedevice 106 and capture device software. The capture device software isthe capture device UI 224 and the document fingerprint matching module226 (e.g., shown in FIG. 3). The capture device software is executed oncapture device 106, or alternatively, on an external server, such asnetworked media server 114 or service provider server 122, that isaccessible to capture device 106. In this embodiment, a networkedservice is available that supplies the data that is linked to thepublications. A hierarchical recognition scheme may be used, in which apublication is first identified and then the page and section within thepublication are identified.

A second alternate embodiment of the MMR system 100 includes capturedevice 106, capture device software and document use software. Thesecond alternate embodiment includes software, such as is shown anddescribed with reference to FIG. 4, that captures and indexes printeddocuments and links basic document events, such as the edit history of adocument. This allows MMR user 110 to point his/her capture device 106at any printed document and determine the name and location of thesource file 310 that generated the document, as well as determine thetime and place of printing.

A third alternate embodiment of the MMR system 100 includes capturedevice 106, capture device software, document use software, and eventcapture module 324. The event capture module 324 is added to MMRcomputer 112 that captures events that are associated with documents,such as the times when they were visible on the desktop of MMR computer112 (determined by monitoring the GDI character generator), URLs thatwere accessed while the documents were open, or characters typed on thekeyboard while the documents were open.

A fourth alternate embodiment of the MMR system 100 includes capturedevice 106, capture device software, and the printer 116. In this fourthalternate embodiment the printer 116 is equipped with a Bluetoothtransceiver or similar communication link that communicates with capturedevice 106 of any MMR user 110 that is in close proximity. Whenever anyMMR user 110 picks up a document from the printer 116, the printer 116pushes the MMR data (document layout and multimedia clips) to thatuser's capture device 106. User printer 116 includes a keypad, by whicha user logs in and enters a code, in order to obtain the multimedia datathat is associated with a specific document. The document may include aprinted representation of a code in its footer, which may be inserted byprinter driver 316.

A fifth alternate embodiment of the MMR system 100 includes capturedevice 106, capture device software, and office portal 120. The officeportal device is preferably a personalized version of office portal 120.The office portal 120 captures events in the office, such asconversations, conference/telephone calls, and meetings. The officeportal 120 identifies and tracks specific paper documents on thephysical desktop. The office portal 120 additionally executes thedocument identification software (i.e., document fingerprint matchingmodule 226 and hosts document event database 320). This fifth alternateembodiment serves to off-load the computing workload from MMR computer112 and provides a convenient way to package MMR system 100 b as aconsumer device (e.g., MMR system 100 b is sold as a hardware andsoftware product that is executing on a Mac Mini computer, by AppleComputer, Inc.).

A sixth alternate embodiment of the MMR system 100 includes capturedevice 106, capture device software, and the networked media server 114.In this embodiment, the multimedia data is resident on the networkedmedia server 114, such as the Comcast Video-on-Demand server. When MMRuser 110 scans a patch of document text by use of his/her capture device106, the resultant lookup command is transmitted either to the set-topbox 126 that is associated with cable TV of MMR user 110 (wirelessly,over the Internet, or by calling set-top box 126 on the phone) or to theComcast server. In both cases, the multimedia is streamed from theComcast server to set-top box 126. The system 100 knows where to sendthe data, because MMR user 110 registered previously his/her phone.Thus, the capture device 106 can be used for access and control of theset-top box 126.

A seventh alternate embodiment of the MMR system 100 includes capturedevice 106, capture device software, the networked media server 114 anda location service. In this embodiment, the location-aware servicediscriminates between multiple destinations for the output from theComcast system (or other suitable communication system). This functionis performed either by discriminating automatically between cellularphone tower IDs or by a keypad interface that lets MMR user 110 choosethe location where the data is to be displayed. Thus, the user canaccess programming and other cable TV features provided by their cableoperator while visiting another location so long as that other locationhas cable access.

Document Fingerprint Matching (“Image-Based Patch Recognition”)

As previously described, document fingerprint matching involves uniquelyidentifying a portion, or “patch”, of an MMR document. Referring to FIG.6, a document fingerprint matching module/system 610 receives a capturedimage 612. The document fingerprint matching system 610 then queries acollection of pages in a document database 3400 (further described belowwith reference to, for example, FIG. 34A) and returns a list of thepages and documents that contain them within which the captured image612 is contained. Each result is an x-y location where the capturedinput image 612 occurs. Those skilled in the art will note that thedatabase 3400 can be external to the document fingerprint matchingmodule 610 (e.g., as shown in FIG. 6), but can also be internal to thedocument fingerprint matching module 610 (e.g., as shown in FIGS. 7, 11,12, 14, 20, 24, 26, 28, and 30-32, where the document fingerprintmatching module 610 includes database 3400).

FIG. 7 shows a block diagram of a document fingerprint matching system610 in accordance with an embodiment of the present invention. A capturedevice 106 captures an image. The captured image is sent to a qualityassessment module 712, which effectively makes a preliminary judgmentabout the content of the captured image based on the needs andcapabilities of downstream processing. For example, if the capturedimage is of such quality that it cannot be processed downstream in thedocument fingerprint matching system 610, the quality assessment module712 causes the capture device 106 to recapture the image at a higherresolution. Further, the quality assessment module 712 may detect manyother relevant characteristics of the captured image such as, forexample, the sharpness of the text contained in the captured image,which is an indication of whether the captured image is “in focus.”Further, the quality assessment module 712 may determine whether thecaptured image contains something that could be part of a document. Forexample, an image patch that contains a non-document image (e.g., adesk, an outdoor scene) indicates that the user is transitioning theview of the capture device 106 to a new document.

Further, in one or more embodiments, the quality assessment module 712may perform text/non-text discrimination so as to pass through onlyimages that are likely to contain recognizable text. FIG. 8 shows a flowprocess for text/non-text discrimination in accordance with one or moreembodiments. A number of columns of pixels are extracted from an inputimage patch at step 810. Typically, an input image is gray-scale, andeach value in the column is an integer from zero to 255 (for 8 bitpixels). At step 812, the local peaks in each column are detected. Thiscan be done with the commonly understood “sliding window” method inwhich a window of fixed length (e.g., N pixels) is slid over the column,M pixels at a time, where M<N. At each step, the presence of a peak isdetermined by looking for a significant difference in gray level values(e.g., greater than 40). If a peak is located at one position of thewindow, the detection of other peaks is suppressed whenever the slidingwindow overlaps this position. The gaps between successive peaks mayalso be detected at step 812. Step 812 is applied to a number C ofcolumns in the image patch, and the gap values are accumulated in ahistogram at step 814.

The gap histogram is compared to other histograms derived from trainingdata with known classifications (at step 816) stored in database 818,and a decision about the category of the patch (either text or non-text)is output together with a measure of the confidence in that decision.The histogram classification at step 816 takes into account the typicalappearance of a histogram derived from an image of text and that itcontains two tight peaks, one centered on the distance between lineswith possibly one or two other much smaller peaks at integer multipleshigher in the histogram away from those peaks. The classification maydetermine the shape of the histogram with a measure of statisticalvariance, or it may compare the histogram one-by-one to storedprototypes with a distance measure, such as, for example, a Hamming orEuclidean distance.

Now referring also to FIG. 9, it shows an example of text/non-textdiscrimination. An input image 910 is processed to sample a number ofcolumns, a subset of which is indicated with dotted lines. The graylevel histogram for a typical column 912 is shown in 914. Y values aregray levels in 910 and the X values are rows in 910. The gaps that aredetected between peaks in the histogram are shown in 916. The histogramof gap values from all sampled columns is shown in 918. This exampleillustrates the shape of a histogram derived from a patch that containstext.

A flow process for estimating the point size of text in an image patchis shown in FIG. 10. This flow process takes advantage of the fact thatthe blur in an image is inversely proportional to the capture device'sdistance from the page. By estimating the amount of blur, the distancemay be estimated, and that distance may be used to scale the size ofobjects in the image to known “normalized” heights. This behavior may beused to estimate the point size of text in a new image.

In a training phase 1010, an image of a patch of text (referred to as a“calibration” image) in a known font and point size is obtained with animage capture device at a known distance at step 1012. The height oftext characters in that image as expressed in a number of pixels ismeasured at step 1014. This may be done, for example, manually with animage annotation tool such as Microsoft Photo Editor. The blur in thecalibration image is estimated at step 1016. This may be done, forexample, with known measurements of the spectral cutoff of thetwo-dimensional fast Fourier transform. This may also be expressed inunits as a number of pixels 1020.

When presented a “new” image at step 1024, as in an MMR recognitionsystem at run-time, the image is processed at step 1026 to locate textwith commonly understood method of line segmentation and charactersegmentation that produces bounding boxes around each character. Theheights of those boxes may be expressed in pixels. The blur of the newimage is estimated at step 1028 in a similar manner as at step 1016.These measures are combined at step 1030 to generate a first estimate1032 of the point size of each character (or equivalently, each line).This may be done by calculating the following equation: (calibrationimage blur size/new image blur size)*(new image text height/calibrationimage text height)*(calibration image font size in points). This scalesthe point size of the text in the calibration image to produce anestimated point size of the text in the input image patch. The samescaling function may be applied to the height of every character'sbounding box. This produces a decision for every character in a patch.For example, if the patch contains 50 characters, this procedure wouldproduce 50 votes for the point size of the font in the patch. A singleestimate for the point size may then be derived with the median of thevotes.

Further, more specifically referring back to FIG. 7, in one or moreembodiments, feedback of the quality assessment module 712 to thecapture device 106 may be directed to a user interface (UI) of thecapture device 106. For example, the feedback may include an indicationin the form of a sound or vibration that indicates that the capturedimage contains something that looks like text but is blurry and that theuser should steady the capture device 106. The feedback may also includecommands that change parameters of the optics of the capture device 106to improve the quality of the captured image. For example, the focus,F-stop, and/or exposure time may be adjusted so at to improve thequality of the captured image.

Further, the feedback of the quality assessment module 712 to thecapture device 106 may be specialized by the needs of the particularfeature extraction algorithm being used. As further described below,feature extraction converts an image into a symbolic representation. Ina recognition system that computes the length of words, it may desirablefor the optics of the capture device 106 to blur the captured image.Those skilled in the art will note that such adjustment may produce animage that, although perhaps not recognizable by a human or an opticalcharacter recognition (OCR) process, is well suited for the featureextraction technique. The quality assessment module 712 may implementthis by feeding back instructions to the capture device 106 causing thecapture device 106 to defocus the lens and thereby produce blurryimages.

The feedback process is modified by a control structure 714. In general,the control structure 714 receives data and symbolic information fromthe other components in the document fingerprint matching system 610.The control structure 714 decides the order of execution of the varioussteps in the document fingerprint matching system 610 and can optimizethe computational load. The control structure 714 identifies the x-yposition of received image patches. More particularly, the controlstructure 714 receives information about the needs of the featureextraction process, the results of the quality assessment module 712,and the capture device 106 parameters, and can change them asappropriate. This can be done dynamically on a frame-by-frame basis. Ina system configuration that uses multiple feature extractionmethodologies, one might require blurry images of large patches of textand another might need high resolution sharply focused images of papergrain. In such a case, the control structure 714 may send commands tothe quality assessment module 712 that instruct it to produce theappropriate image quality when it has text in view. The qualityassessment module 712 would interact with the capture device 106 toproduce the correct images (e.g., N blurry images of a large patchfollowed by M images of sharply focused paper grain (high resolution)).The control structure 714 would track the progress of those imagesthrough the processing pipeline to ensure that the corresponding featureextraction and classification is applied.

An image processing module 716 modifies the quality of the input imagesbased on the needs of the recognition system. Examples of types of imagemodification include sharpening, deskewing, and binarization. Suchalgorithms include many tunable parameters such as mask sizes, expectedrotations, and thresholds.

As shown in FIG. 7, the document fingerprint matching system 610 usesfeedback from feature extraction and classification modules 718, 720(described below) to dynamically modify the parameters of the imageprocessing module 716. This works because the user will typically pointtheir capture device 106 at the same location in a document for severalseconds continuously. Given that, for example, the capture device 106processes 30 frames per second, the results of processing the first fewframes in any sequence can affect how the frames captured later areprocessed.

A feature extraction module 718 converts a captured image into asymbolic representation. In one example, the feature extraction module718 locates words and computes their bounding boxes. In another example,the feature extraction module 718 locates connected components andcalculates descriptors for their shape. Further, in one or moreembodiments, the document fingerprint matching system 610 sharesmetadata about the results of feature extraction with the controlstructure 714 and uses that metadata to adjust the parameters of othersystem components. Those skilled in the art will note that this maysignificantly reduce computational requirements and improve accuracy byinhibiting the recognition of poor quality data. For example, a featureextraction module 718 that identifies word bounding boxes could tell thecontrol structure 714 the number of lines and “words” it found. If thenumber of words is too high (indicating, for example, that the inputimage is fragmented), the control structure 714 could instruct thequality assessment module 712 to produce blurrier images. The qualityassessment module 712 would then send the appropriate signal to thecapture device 106. Alternatively, the control structure 714 couldinstruct the image processing module 716 to apply a smoothing filter.

A classification module 720 converts a feature description from thefeature extraction module 718 into an identification of one or morepages within a document and the x,y positions within those pages wherean input image patch occurs. The identification is made dependent onfeedback from a database 3400 as described in turn. Further, in one ormore embodiments, a confidence value may be associated with eachdecision. The document fingerprint matching system 610 may use suchdecisions to determine parameters of the other components in the system.For example, the control structure 714 may determine that if theconfidences of the top two decisions are close to one another, theparameters of the image processing algorithms should be changed. Thiscould result in increasing the range of sizes for a median filter andthe carry-through of its results downstream to the rest of thecomponents.

Further, as shown in FIG. 7, there may be feedback between theclassification module 720 and a database 3400. Further, those skilled inthe art will recall that database 3400 can be external to the module 610as shown in FIG. 6. A decision about the identity of a patch can be usedto query the database 3400 for other patches that have a similarappearance. This would compare the perfect image data of the patchstored in the database 3400 to other images in the database 3400 ratherthan comparing the input image patch to the database 3400. This mayprovide an additional level of confirmation for the classificationmodule's 720 decision and may allow some preprocessing of matching data.

The database comparison could also be done on the symbolicrepresentation for the patch rather than only the image data. Forexample, the best decision might indicate the image patch contains a12-point Arial font double-spaced. The database comparison could locatepatches in other documents with a similar font, spacing, and word layoutusing only textual metadata rather than image comparisons.

The database 3400 may support several types of content-based queries.The classification module 720 can pass the database 3400 a featurearrangement and receive a list of documents and x-y locations where thatarrangement occurs. For example, features might be trigrams (describedbelow) of word lengths either horizontally or vertically. The database3400 could be organized to return a list of results in response toeither type of query. The classification module 720 or the controlstructure 714 could combine those rankings to generate a single sortedlist of decisions.

Further, there may be feedback between the database 3400, theclassification module 720, and the control structure 714. In addition tostoring information sufficient to identify a location from a featurevector, the database 3400 may store related information including apristine image of the document as well as a symbolic representation forits graphical components. This allows the control structure 714 tomodify the behavior of other system components on-the-fly. For example,if there are two plausible decisions for a given image patch, thedatabase 3400 could indicate that they could be disambiguated by zoomingout and inspecting the area to the right for the presence of an image.The control structure 714 could send the appropriate message to thecapture device 106 instructing it to zoom out. The feature extractionmodule 718 and the classification module 720 could inspect the rightside of the image for an image printed on the document.

Further, it is noted that the database 3400 stores detailed informationabout the data surrounding an image patch, given that the patch iscorrectly located in a document. This may be used to trigger furtherhardware and software image analysis steps that are not anticipated inthe prior art. That detailed information is provided in one case by aprint capture system that saves a detailed symbolic description of adocument. In one or more other embodiments, similar information may beobtained by scanning a document.

Still referring to FIG. 7, a position tracking module 724 receivesinformation about the identity of an image patch from the controlstructure 714. The position tracking module 724 uses that to retrieve acopy of the entire document page or a data structure describing thedocument from the database 3400. The initial position is an anchor forthe beginning of the position tracking process. The position trackingmodule 724 receives image data from the capture device 106 when thequality assessment module 712 decides the captured image is suitable fortracking. The position tracking module 724 also has information aboutthe time that has elapsed since the last frame was successfullyrecognized. The position tracking module 724 applies an optical flowtechnique which allows it to estimate the distance over the document thecapture device 106 has been moved between successive frames. Given thesampling rate of the capture device 106, its target can be estimatedeven though data it sees may not be recognizable. The estimated positionof the capture device 106 may be confirmed by comparison of its imagedata with the corresponding image data derived from the databasedocument. A simple example computes a cross correlation of the capturedimage with the expected image in the database 3400.

Thus, the position tracking module 724 provides for the interactive useof database images to guide the progress of the position trackingalgorithm. This allows for the attachment of electronic interactions tonon-text objects such as graphics and images. Further, in one or moreother embodiments, such attachment may be implemented without the imagecomparison/confirmation step described above. In other words, byestimating the instant motion of the capture device 106 over the page,the electronic link that should be in view independent of the capturedimage may be estimated.

FIG. 11 shows a document fingerprint matching technique in accordancewith an embodiment of the present invention. The “feed-forward”technique shown in FIG. 11 processes each patch independently. Itextracts features from an image patch that are used to locate one ormore pages and the x-y locations on those pages where the patch occurs.For example, in one or more embodiments, feature extraction for documentfingerprint matching may depend on the horizontal and vertical groupingof features (e.g., words, characters, blocks) of a captured image. Thesegroups of extracted features may then be used to look up the documents(and the patches within those documents) that contain the extractedfeatures. OCR functionality may be used to identify horizontal wordpairs in a captured image. Each identified horizontal word pair is thenused to form a search query to database 3400 for determining all thedocuments that contain the identified horizontal word pair and the x-ylocations of the word pair in those documents. For example, for thehorizontal word pair “the, cat”, the database 3400 may return (15, x,y), (20, x, y), indicating that the horizontal word pair “the, cat”occurs in document 15 and 20 at the indicated x-y locations. Similarly,for each vertically adjacent word pair, the database 3400 is queried forall documents containing instances of the word pair and the x-ylocations of the word pair in those documents. For example, for thevertically adjacent word pair “in, hat”, the database 3400 may return(15, x, y), (7, x, y), indicating that the vertically adjacent word pair“in, hat” occurs in documents 15 and 7 at the indicated x-y locations.Then, using the document and location information returned by thedatabase 3400, a determination can be made as to which document the mostlocation overlap occurs between the various horizontal word pairs andvertically adjacent word pairs extracted from the captured image. Thismay result in identifying the document which contains the capturedimage, in response to which presence of a hot spot and linked media maybe determined.

FIG. 12 shows another document fingerprint matching technique inaccordance with an embodiment of the present invention. The “interactiveimage analysis” technique shown in FIG. 12 involves the interactionbetween image processing and feature extraction that may occur before animage patch is recognized. For example, the image processing module 716may first estimate the blur in an input image. Then, the featureextraction module 718 calculates the distance from the page and pointsize of the image text. Then, the image processing module 716 mayperform a template matching step on the image using characteristics offonts of that point size. Subsequently, the feature extraction module718 may then extract character or word features from the result.Further, those skilled in the art will recognize that the fonts, pointsizes, and features may be constrained by the fonts in the database 3400documents.

An example of interactive image analysis as described above withreference to FIG. 12 is shown in FIG. 13. An input image patch isprocessed at step 1310 to estimate the font and point size of text inthe image patch as well as its distance from the camera. Those skilledin the art will note that font estimation (i.e., identification ofcandidates for the font of the text in the patch) may be done with knowntechniques. Point size and distance estimation may be performed, forexample, using the flow process described with reference to FIG. 10.Further, other techniques may be used such as known methods of distancefrom focus that could be readily adapted to the capture device.

Still referring to FIG. 13, a line segmentation algorithm is applied atstep 1312 that constructs a bounding box around the lines of text in thepatch. The height of each line image is normalized to a fixed size atstep 1314 using known techniques such as proportional scaling. Theidentity for the font detected in the image as well as its point sizeare passed 1324 to a collection of font prototypes 1322, where they areused to retrieve image prototypes for the characters in each named font.

The font database 1322 may be constructed from the font collection on auser's system that is used by the operating system and other softwareapplications to print documents (e.g., TrueType, OpenType, or rasterfonts in Microsoft Windows). In one or more other embodiments, the fontcollection may be generated from pristine images of documents indatabase 3400. The database 3400 xml files provide x-y bounding boxcoordinates that may be used to extract prototype images of charactersfrom the pristine images. The xml file identifies the name of the fontand the point size of the character exactly.

The character prototypes in the selected fonts are size normalized atstep 1320 based on a function of the parameters that were used at step1314. Image classification at step 1316 may compare the size normalizedcharacters outputted at step 1320 to the output at step 1314 to producea decision at each x-y location in the image patch. Known methods ofimage template matching may be used to produce output such as (ci, xi,yi, wi, hi), where ci is identity of a character, (xi yi) is the upperleft corner of its bounding box, and hi, wi is its width and height, forevery character i, i=1 . . . n detected in the image patch.

At step 1318, the geometric relation-constrained database lookup can beperformed as described above, but may be specialized in a case for pairsof characters instead of pairs of words. In such cases: “a−b” mayindicate that the characters a and b are horizontally adjacent; “a+b”may indicate that they are vertically adjacent; “a/b” may indicate thata is southwest of b; and “a\b” may indicate a is southeast of b. Thegeometric relations may be derived from the xi yi values of each pair ofcharacters. The MMR database 3400 may be organized so that it returns alist of document pages that contain character pairs instead of wordpairs. The output at step 1326 is a list of candidates that match theinput image expressed as n-tuples ranked by score (documenti, pagei, xi,yi, actioni, scorei).

FIG. 14 shows another document fingerprint matching technique inaccordance with an embodiment of the present invention. The “generateand test” technique shown in FIG. 14 processes each patch independently.It extracts features from an image patch that are used to locate anumber of page images that could contain the given image patch. Further,in one or more embodiments, an additional extraction-classification stepmay be performed to rank pages by the likelihood that they contain theimage patch.

Still referring to the “generate and test” technique described abovewith reference to FIG. 14, features of a captured image may be extractedand the document patches in the database 3400 that contain the mostnumber of these extracted features may be identified. The first Xdocument patches (“candidates”) with the most matching features are thenfurther processed. In this processing, the relative locations offeatures in the matching document patch candidate are compared with therelative locations of features in the query image. A score is computedbased on this comparison. Then, the highest score corresponding to thebest matching document patch P is identified. If the highest score islarger than an adaptive threshold, then document patch P is found asmatching to the query image. The threshold is adaptive to manyparameters, including, for example, the number of features extracted. Inthe database 3400, it is known where the document patch P comes from,and thus, the query image is determined as coming from the samelocation.

FIG. 15 shows an example of a word bounding box detection algorithm. Aninput image patch 1510 is shown after image processing that corrects forrotation. Commonly known as a skew correction algorithm, this class oftechnique rotates a text image so that it aligns with the horizontalaxis. The next step in the bounding box detection algorithm is thecomputation of the horizontal projection profile 1512. A threshold forline detection is chosen 1516 by known adaptive thresholding or slidingwindow algorithms in such a way that the areas “above threshold”correspond to lines of text. The areas within each line are extractedand processed in a similar fashion 1514 and 1518 to locate areas abovethreshold that are indicative of words within lines. An example of thebounding boxes detected in one line of text is shown in 1520.

Various features may be extracted for comparison with document patchcandidates. For example, Scale Invariant Feature Transform (SIFT)features, corner features, salient points, ascenders, and descenders,word boundaries, and spaces may be extracted for matching. One of thefeatures that can be reliably extracted from document images is wordboundaries. Once word boundaries are extracted, they may be formed intogroups as shown in FIG. 16. In FIG. 16, for example, vertical groups areformed in such a way that a word boundary has both above and belowoverlapping word boundaries, and the total number of overlapping wordboundaries is at least 3 (noting that the minimum number of overlappingword boundaries may differ in one or more other embodiments). Forexample, a first feature point (second word box in the second line,length of 6) has two word boundaries above (lengths of 5 and 7) and oneword boundary below (length of 5). A second feature point (fourth wordbox in the third line, length of 5) has two word boundaries above(lengths of 4 and 5) and two word boundaries below (lengths of 8 and 7).Thus, as shown in FIG. 16, the indicated features are represented withthe length of the middle word boundary, followed by the lengths of theabove word boundaries and then by lengths of the below word boundaries.Further, it is noted that the lengths of the word boxes may be based onany metric. Thus, it is possible to have alternate lengths for some wordboxes. In such cases, features may be extracted containing all or someof their alternates.

Further, in one or more embodiments, features may be extracted such thatspaces are represented with 0s and word regions are represented with 1s.An example is shown in FIG. 17. The block representations on the rightside correspond to word/space regions of the document patch on the leftside.

Extracted features may be compared with various distance measures,including, for example, norms and Hamming distance. Alternatively, inone or more embodiments, hash tables may be used to identify documentpatches that have the same features as the query image. Once suchpatches are identified, angles from each feature point to other featurepoints may be computed as shown in FIG. 18. Alternatively, anglesbetween groups of feature points may be calculated. 1802 shows theangles 1803, 1804, and 1805 calculated from a triple of feature points.The computed angles may then be compared to the angles from each featurepoint to other feature points in the query image. If any angles formatching points are similar, then a similarity score may be increased.Alternatively, if groups of angles are used, and if groups of anglesbetween similar groups of feature points in two images are numericallysimilar, then a similarity score is increased. Once the scores arecomputed between the query image to each retrieved document patch, thedocument patch resulting in the highest score is selected and comparedto an adaptive threshold to determine whether the match meets somepredetermined criteria. If the criteria is met, then a matching documentpath is indicated as being found.

Further, in one or more embodiments, extracted features may be based onthe length of words. Each word is divided into estimated letters basedon the word height and width. As the word line above and below a givenword are scanned, a binary value is assigned to each of the estimatedletters according the space information in the lines above and below.The binary code is then represented with an integer number. For example,referring to FIG. 19, it shows an arrangement of word boxes eachrepresenting a word detected in a captured image. The word 1910 isdivided into estimated letters. This feature is described with (i) thelength of the word 1910, (ii) the text arrangement of the line above theword 1910, and (iii) the text arrangement of the line below the word1910. The length of the word 1910 is measured in numbers of estimatedletters. The text arrangement information is extracted from binarycoding of the space information above or below the current estimatedletter. In word 1910, only the last estimated letter is above a space;the second and third estimated letters are below a space. Accordingly,the feature of word 1910 is coded as (6, 100111, 111110), where 0 meansspace, and 1 means no space. Rewritten in integer form, word 1910 iscoded (6, 39, 62).

FIG. 20 shows another document fingerprint matching technique inaccordance with an embodiment of the present invention. The “multipleclassifiers” technique shown in FIG. 20 leverages the complementaryinformation of different feature descriptions by classifying themindependently and combining the results. An example of this paradigmapplied to text patch matching is extracting the lengths of horizontallyand vertically adjacent pairs of words and computing a ranking of thepatches in the database separately. More particularly, for example, inone or more embodiments, the locations of features are determined by“classifiers” attendant with the classification module 720. A capturedimage is fingerprinted using a combination of classifiers fordetermining horizontal and vertical features of the captured image. Thisis performed in view of the observation that an image of text containstwo independent sources of information as to its identity—in addition tothe horizontal sequence of words, the vertical layout of the words canalso be used to identity the document from which the image wasextracted. For example, as shown in FIG. 21, a captured image 2110 isclassified by a horizontal classifier 2112 and a vertical classifier2114. Each of the classifiers 2112, 2114, in addition to inputting thecaptured image, takes information from a database 3400 to in turn outputa ranking of those document pages to which the respectiveclassifications may apply. In other words, the multi-classifiertechnique shown in FIG. 21 independently classifies a captured imageusing horizontal and vertical features. The ranked lists of documentpages are then combined according to a combination algorithm 2118(examples further described below), which in turn outputs a ranked listof document pages, the list being based on both the horizontal andvertical features of the captured image 2110. Particularly, in one ormore embodiments, the separate rankings from the horizontal classifier2112 and the vertical classifier 2114 are combined using informationabout how the detected features co-occur in the database 3400.

Now also referring to FIG. 22, it shows an example of how verticallayout is integrated with horizontal layout for feature extraction. In(a), a captured image 2200 with word divisions is shown. From thecaptured image 2200, horizontal and vertical “n-grams” are determined.An “n-gram” is a sequence of n numbers each describing a quantity ofsome characteristic. For example, a horizontal trigram specifies thenumber of characters in each word of a horizontal sequence of threewords. For example, for the captured image 2200, (b) shows horizontaltrigrams: 5-8-7 (for the number of characters in each of thehorizontally sequenced words “upper”, “division”, and “courses” in thefirst line of the captured image 2200); 7-3-5 (for the number ofcharacters in each of the horizontally sequenced words “Project”, “has”,and “begun” in the second line of the captured image 2200); 3-5-3 (forthe number of characters in each of the horizontally sequenced words“has”, “begun”, and “The” in the second line of the captured image2200); 3-3-6 (for the number of characters in each of the horizontallysequenced words “461”, “and”, and “permit” in the third line of thecaptured image 2200); and 3-6-8 (for the number of characters in each ofthe horizontally sequenced words “and”, “permit”, and “projects” in thethird line of the captured image 2200).

A vertical trigram specifies the number of characters in each word of avertical sequence of words above and below a given word. For example,for the captured image 2200, (c) shows vertical trigrams: 5-7-3 (for thenumber of characters in each of the vertically sequenced words “upper”,“Project”, and “461”); 8-7-3 (for the number of characters in each ofthe vertically sequenced words “division”, “Project”, and “461”); 8-3-3(for the number of characters in each of the vertically sequenced words“division”, “has”, and “and”); 8-3-6 (for the number of characters ineach of the vertically sequenced words “division”, “has”, and “permit”);8-5-6 (for the number of characters in each of the vertically sequencedwords “division”, “begun”, and “permit”); 8-5-8 (for the number ofcharacters in each of the vertically sequenced words “division”,“begun”, and “projects”); 7-5-6 (for the number of characters in each ofthe vertically sequenced words “courses”, “begun”, and “permit”); 7-5-8(for the number of characters in each of the vertically sequenced words“courses”, “begun”, and “projects”); 7-3-8 (for the number of charactersin each of the vertically sequenced words “courses”, “The”, and“projects”); 7-3-7 (for the number of characters in each of thevertically sequenced words “Project”, “461”, and “student”); and 3-3-7(for the number of characters in each of the vertically sequenced words“has”, “and”, and “student”).

Based on the determined horizontal and vertical trigrams from thecaptured image 2200 shown in FIG. 22, lists of documents (d) and (e) aregenerated indicating the documents the contain each of the horizontaland vertical trigrams. For example, in (d), the horizontal trigram 7-3-5occurs in documents 15, 22, and 134. Further, for example, in (e), thevertical trigram 7-5-6 occurs in documents 15 and 17. Using thedocuments lists of (d) and (e), a ranked list of all the referenceddocuments are respectively shown in (f) and (g). For example, in (f),document 15 is referenced by five horizontal trigrams in (d), whereasdocument 9 is only referenced by one horizontal trigram in (d). Further,for example, in (g), document 15 is referenced by eleven verticaltrigrams in (e), whereas document 18 is only referenced by one verticaltrigram in (e).

Now also referring to FIG. 23, it shows a technique for combining thehorizontal and vertical trigram information described with reference toFIG. 22. The technique combines the lists of votes from the horizontaland vertical feature extraction using information about the knownphysical location of trigrams on the original printed pages. For everydocument in common among the top M choices outputted by each of thehorizontal and vertical classifiers, the location of every horizontaltrigram that voted for the document is compared to the location of everyvertical trigram that voted for that document. A document receives anumber of votes equal to the number of horizontal trigrams that overlapany vertical trigram, where “overlap” occurs when the bounding boxes oftwo trigrams overlap. In addition, the x-y positions of the centers ofoverlaps are counted with a suitably modified version of the evidenceaccumulation algorithm described below with reference to 3406 of FIGS.34A. For example, as shown in FIG. 23, the lists in (a) and (b)(respectively (f) and (g) in FIG. 22) are intersected to determine alist of pages (c) that are both referenced by horizontal and verticaltrigrams. Using the intersected list (c), lists (d) and (e) (showingonly the intersected documents as referenced to by the identifiedtrigrams), and a printed document database 3400, an overlap of documentsis determined. For example, document 6 is referenced by horizontaltrigram 3-5-3 and by vertical trigram 8-3-6, and those two trigramsthemselves overlap over the word “has” in the captured image 2200; thusdocument 6 receives one vote for the one overlap. As shown in (f), forthe particular captured image 2200, document 15 receives the most numberof votes and is thus identified as the document containing the capturedimage 2200. (x1, y1) is identified as the location of the input imagewithin document 15. Thus, in summary of the document fingerprintmatching technique described above with reference to FIGS. 22 and 23, ahorizontal classifier uses features derived from the horizontalarrangement of words of text, and a vertical classifier uses featuresderived from the vertical arrangement of those words, where the resultsare combined based on the overlap of those features in the originaldocuments. Such feature extraction provides a mechanism for uniquelyidentifying documents in that while the horizontal aspects of thisfeature extraction are subject to the constraints of proper grammar andlanguage, the vertical aspects are not subject to such constraints.

Further, although the description with reference to FIGS. 22 and 23 isparticular to the use of trigrams, any n-gram may be used for one orboth of horizontal and vertical feature extraction/classification. Forexample, in one or more embodiments, vertical and horizontal n-grams,where n=4, may be used for multi-classifier feature extraction. In oneor more other embodiments, the horizontal classifier may extractfeatures based on n-grams, where n=3, whereas the vertical classifiermay extract features based on n-grams, where n=5.

Further, in one or more embodiments, classification may be based onadjacency relationships that are not strictly vertical or horizontal.For example, NW, SW, NW, and SE adjacency relationships may be used forextraction/classification.

FIG. 24 shows another document fingerprint matching technique inaccordance with an embodiment of the present invention. The“database-driven feedback” technique shown in FIG. 24 takes intoconsideration that the accuracy of a document image matching system maybe improved by utilizing the images of the documents that could matchthe input to determine a subsequent step of image analysis in whichsub-images from the pristine documents are matched to the input image.The technique includes a transformation that duplicates the noisepresent in the input image. This may be followed by a template matchinganalysis.

FIG. 25 shows a flow process for database-driven feedback in accordancewith an embodiment of the present invention. An input image patch isfirst preprocessed and recognized at steps 2510, 2512 as described above(e.g., using word OCR and word-pair lookup, character OCR and characterpair lookup, word bounding box configuration) to produce a number ofcandidates for the identification of an image patch 2522. Each candidatein this list may contain the following items (doci, pagei, xi, yi),where doci is an identifier for a document, pagei a page within thedocument, and (xi, yi) is the x-y coordinates of the center of the imagepatch within that page.

A pristine patch retrieval algorithm at step 2514 normalizes the size ofthe entire input image patch to a fixed size optionally using knowledgeof the distance from the page to ensure that it is transformed to aknown spatial resolution, e.g., 100 dpi. The font size estimationalgorithm described above may be adapted to this task. Similarly, knowndistance from focus or depth from focus techniques may be used. Also,size normalization can proportionally scale the image patches based onthe heights of their word bounding boxes.

The pristine patch retrieval algorithm queries the MMR database 3400with the identifier for each document and page it receives together withthe center of the bounding box for a patch that the MMR database willgenerate. The extent of the generated patch depends on the size of thenormalized input patch. In such a manner, patches of the same spatialresolution and dimensions may be obtained. For example, when normalizedto 100 dpi, the input patch can extend 50 pixels on each side of itscenter. In this case, the MMR database would be instructed to generate a100 dpi pristine patch that is 100 pixels high and wide centered at thespecified x-y value.

Each pristine image patch returned from the MMR database 2524 may beassociated with the following items (doci, pagei, xi, yi, widthi,heighti, actioni), where (doci, pagei, xi, yi) are as described above,widthi and heighti are the width and height of the pristine patch inpixels, and actioni is an optional action that might be associated withthe corresponding area in doci's entry in the database. The pristinepatch retrieval algorithm outputs 2518 this list of image patches anddata 2518 together with the size normalized input patch it constructed.

Further, in one or more embodiments, the patch matching algorithm 2516compares the size normalized input patch to each pristine patch andassigns a score 2520 that measures how well they match one another.Those skilled in the art will appreciate that a simple cross correlationto a Hamming distance suffices in many cases because of the mechanismsused to ensure that sizes of the patches are comparable. Further, thisprocess may include the introduction of noise into the pristine patchthat mimics the image noise detected in the input. The comparison couldalso be arbitrarily complex and could include a comparison of anyfeature set including the OCR results of the two patches and a rankingbased on the number of characters, character pairs, or word pairs wherethe pairs could be constrained by geometric relations as before.However, in this case, the number of geometric pairs in common betweenthe input patch and the pristine patch may be estimated and used as aranking metric.

Further, the output 2520 may be in the form of n-tuples (doci, pagei,xi, yi, actioni, scorei), where the score is provided by the patchmatching algorithm and measures how well the input patch matches thecorresponding region of doci, pagei.

FIG. 26 shows another document fingerprint matching technique inaccordance with an embodiment of the present invention. The“database-driven classifier” technique shown in FIG. 26 uses an initialclassification to generate a set of hypotheses that could contain theinput image. Those hypotheses are looked up in the database 3400 and afeature extraction plus classification strategy is automaticallydesigned for those hypotheses. An example is identifying an input patchas containing either a Times or Arial font. In this case, the controlstructure 714 invokes a feature extractor and classifier specialized forserif/san serif discrimination.

FIG. 27 shows a flow process for database-driven classification inaccordance with an embodiment of the present invention. Following afirst feature extraction 2710, the input image patch is classified 2712by any one or more of the recognition methods described above to producea ranking of documents, pages, and x-y locations within those pages.Each candidate in this list may contain, for example, the followingitems (doci, pagei, xi, yi), where doci is an identifier for a document,pagei a page within the document, and (xi, yi) are the x-y coordinatesof the center of the image patch within that page. The pristine patchretrieval algorithm 2714 described with reference to FIG. 25 may be usedto generate a patch image for each candidate.

Still referring to FIG. 27, a second feature extraction is applied tothe pristine patches 2716. This may differ from the first featureextraction and may include, for example, one or more of a font detectionalgorithm, a character recognition technique, bounding boxes, and SIFTfeatures. The features detected in each pristine patch are inputted toan automatic classifier design method 2720 that includes, for example, aneural network, support vector machine, and/or nearest neighborclassifier that are designed to classify an unknown sample as one of thepristine patches. The same second feature extraction may be applied 2718to the input image patch, and the features it detects are inputted tothis newly designed classifier that may be specialized for the pristinepatches.

The output 2724 may be in the form of n-tuples (doci, pagei, xi, yi,actioni, scorei), where the score is provided by the classificationtechnique 2722 that was automatically designed by 2720. Those skilled inthe art will appreciate that the score measures how well the input patchmatches the corresponding region of doci, pagei.

FIG. 28 shows another document fingerprint matching technique inaccordance with an embodiment of the present invention. The“database-driven multiple classifier” technique shown in FIG. 28 reducesthe chance of a non-recoverable error early in the recognition processby carrying multiple candidates throughout the decision process. Severalinitial classifications are performed. Each generates a differentranking of the input patch that could be discriminated by differentfeature extraction and classification. For example, one of those setsmight be generated by horizontal n-grams and uniquely recognized bydiscriminating serif from san-serif. Another example might be generatedby vertical n-grams and uniquely recognized by accurate calculation ofline separation.

FIG. 29 shows a flow process for database-driven multiple classificationin accordance with an embodiment of the present invention. The flowprocess is similar to that shown in FIG. 27, but it uses multipledifferent feature extraction algorithms 2910 and 2912 to produceindependent rankings of the input image patch with the classifiers 2914and 2916. Examples of features and classification techniques includehorizontal and vertical word-length n-grams described above. Eachclassifier may produce a ranked list of patch identifications thatcontains at least the following items (doci, pagei, xi, yi, scorei) foreach candidate, where doci is an identifier for a document, pagei a pagewithin the document, (xi, yi) are the x-y coordinates of the center ofthe image patch within that page, and scorei measures how well the inputpatch matches the corresponding location in the database document.

The pristine patch retrieval algorithm described above with reference toFIG. 25 may be used to produce a set of pristine image patches thatcorrespond to the entries in the list of patch identifications in theoutput of 2914 and 2916. A third and fourth feature extraction 2918 and2920 may be applied as before to the pristine patches and classifiersautomatically designed and applied as described above in FIG. 27.

Still referring to FIG. 29, the rankings produced by those classifiersare combined to produce a single ranking 2924 with entries (doci, pagei,xi, yi, actioni, scorei) for i=1 . . . number of candidates, and wherethe values in each entry are as described above. The ranking combination2922 may be performed by, for example, a known Borda count measure thatassigns an item a score based on its common position in the tworankings. This may be combined with the score assigned by the individualclassifiers to generate a composite score. Further, those skilled in theart will note that other methods of ranking combination may be used.

FIG. 30 shows another document fingerprint matching technique inaccordance with an embodiment of the present invention. The “videosequence image accumulation” technique shown in FIG. 30 constructs animage by integrating data from nearby or adjacent frames. One exampleinvolves “super-resolution.” It registers N temporally adjacent framesand uses knowledge of the point spread function of the lens to performwhat is essentially a sub-pixel edge enhancement. The effect is toincrease the spatial resolution of the image. Further, in one or moreembodiments, the super-resolution method may be specialized to emphasizetext-specific features such as holes, corners, and dots. A furtherextension would use the characteristics of the candidate image patches,as determined from the database 3400, to specialize the super-resolutionintegration function.

FIG. 31 shows another document fingerprint matching technique inaccordance with an embodiment of the present invention. The “videosequence feature accumulation” technique shown in FIG. 31 accumulatesfeatures over a number of temporally adjacent frames prior to making adecision. This takes advantage of the high sampling rate of a capturedevice (e.g., 30 frames per second) and the user's intention, whichkeeps the capture device pointed at the same point on a document atleast for several seconds. Feature extraction is performed independentlyon each frame and the results are combined to generate a single unifiedfeature map. The combination process includes an implicit registrationstep. The need for this technique is immediately apparent on inspectionof video clips of text patches. The auto-focus and contrast adjustmentin the typical capture device can produce significantly differentresults in adjacent video frames.

FIG. 32 shows another document fingerprint matching technique inaccordance with an embodiment of the present invention. The “videosequence decision combination” technique shown in FIG. 32 combinesdecisions from a number of temporally adjacent frames. This takesadvantage of the high sampling rate of a typical capture device and theuser's intention, which keeps the capture device pointed at the samepoint on a document at least for several seconds. Each frame isprocessed independently and generates its own ranked list of decisions.Those decisions are combined to generate a single unified ranking of theinput image set. This technique includes an implicit registration methodthat controls the decision combination process.

In one or more embodiments, one or more of the various documentfingerprint matching techniques described above with reference to FIGS.6-32 may be used in combination with one or more known matchingtechniques, such combination being referred to herein as “multi-tier (ormulti-factor) recognition.” In general, in multi-tier recognition, afirst matching technique is used to locate in a document database a setof pages having specific criteria, and then a second matching techniqueis used to uniquely identify a patch from among the pages in the set.

FIG. 33 shows an example of a flow process for multi-tier recognition inaccordance with an embodiment of the present invention. Initially, atstep 3310, a capture device 106 is used to capture/scan a “culling”feature on a document of interest. The culling feature may be anyfeature, the capture of which effectively results in a selection of aset of documents within a document database. For example, the cullingfeature may be a numeric-only bar code (e.g., universal product code(UPC)), an alphanumeric bar code (e.g., code 39, code 93, code 128), ora 2-dimensional bar code (e.g., a QR code, PDF417, DataMatrix,Maxicode). Moreover, the culling feature may be, for example, a graphic,an image, a trademark, a logo, a particular color or combination ofcolors, a keyword, or a phrase. Further, in one or more embodiments, aculling feature may be limited to features suitable for recognition bythe capture device 106.

At step 3312, once the culling feature has been captured at step 3310, aset of documents and/or pages of documents in a document database areselected based on an association with the captured culling feature. Forexample, if the captured culling feature is a company's logo, alldocuments in the database indexed as containing that logo are selected.In another example, the database may contain a library of trademarksagainst which captured culling images are compared. When there is a“hit” in the library, all documents associated with the hit trademarkare selected for subsequent matching as described below. Further, in oneor more embodiments, the selection of documents/pages at step 3312 maydepend on the captured culling feature and the location of that cullingfeature on the scanned document. For example, information associatedwith the captured culling feature may specify whether that culling imageis located at the upper right corner of the document as opposed to thelower left corner of the document.

Further, those skilled in the art will note that the determination thata particular captured image contains an image of a culling feature maybe made by the capture device 106 or some other component that receivesraw image data from the capture device 106. For example, the databaseitself may determine that a particular captured image sent from thecapture device 106 contains a culling feature, in response to which thedatabase selects a set of documents associated with the captured cullingfeature.

At step 3314, after a particular set of documents has been selected atstep 3312, the capture device 106 continues to scan and accordinglycapture images of the document of interest. The captured images of thedocument are then matched against the documents selected at step 3312using one or more of the various document fingerprint matchingtechniques described with reference to FIGS. 6-32. For example, after aset of documents indexed as containing the culling feature of a shoegraphic is selected at step 3312 based on capture of a shoe graphicimage on a document of interest at step 3310, subsequent captured imagesof the document of interest may be matched against the set of selecteddocuments using the multiple classifiers technique as previouslydescribed.

Thus, using an implementation of the multi-tier recognition flow processdescribed above with reference to FIG. 33, patch recognition times maybe decreased by initially reducing the amount of pages/documents againstwhich subsequent captured images are matched. Further, a user may takeadvantage of such improved recognition times by first scanning adocument over locations where there is an image, a bar code, a graphic,or other type of culling feature. By taking such action, the user mayquickly reduce the amount of documents against which subsequent capturedimages are matched.

MMR Database System

FIG. 34A illustrates a functional block diagram of an MMR databasesystem 3400 configured in accordance with one embodiment of theinvention. The system 3400 is configured for content-based retrieval,where two-dimensional geometric relationships between objects arerepresented in a way that enables look-up in a text-based index (or anyother searchable indexes). The system 3400 employs evidence accumulationto enhance look-up efficiency by, for example, combining the frequencyof occurrence of a feature with the likelihood of its location in atwo-dimensional zone. In one particular embodiment, the database system3400 is a detailed implementation of the document event database 320(including PD index 322), the contents of which include electronicrepresentations of printed documents generated by a capture module 318and/or a document fingerprint matching module 226 as discussed abovewith reference to FIG. 3. Other applications and configurations forsystem 3400 will be apparent in light of this disclosure.

As can be seen, the database system 3400 includes an MMR index tablemodule 3404 that receives a description computed by the MMR featureextraction module 3402, an evidence accumulation module 3406, and arelational database 3408 (or any other suitable storage facility). Theindex table module 3404 interrogates an index table that identifies thedocuments, pages, and x-y locations within those pages where eachfeature occurs. The index table can be generated, for example, by theMMR index table module 3404 or some other dedicated module. The evidenceaccumulation module 3406 is programmed or otherwise configured tocompute a ranked set of document, page and location hypotheses 3410given the data from the index table module 3404. The relational database3408 can be used to store additional characteristics 3412 about eachpatch. Those include, but are not limited to, 504 and 508 in FIG. 5. Byusing a two-dimensional arrangement of text within a patch in deriving asignature or fingerprint (i.e., unique search term) for the patch, theuniqueness of even a small fragment of text is significantly increased.Other embodiments can similarly utilize any two-dimensional arrangementof objects/features within a patch in deriving a signature orfingerprint for the patch, and embodiments of the invention are notintended to be limited to two-dimensional arrangements of text foruniquely identifying patches. Other components and functionality of thedatabase system 3400 illustrated in FIG. 34A include a feedback-directedfeatures search module 3418, a document rendering application module3414, and a sub-image extraction module 3416. These components interactwith other system 3400 components to provide a feedback-directed featuresearch as well as dynamic pristine image generation. In addition, thesystem 3400 includes an action processor 3413 that receives actions. Theactions determine the action performed by the database system 3400 andthe output it provides. Each of these other components will be explainedin turn.

An example of the MMR feature extraction module 3402 that utilizes thistwo-dimensional arrangement of text within a patch is shown in FIG. 34B.In one such embodiment, the MMR feature extraction module 3402 isprogrammed or otherwise configured to employ an OCR-based technique toextract features (text or other target features) from an image patch. Inthis particular embodiment, the feature extraction module 3402 extractsthe x-y locations of words in an image of a patch of text and representsthose locations as the set of horizontally and vertically adjacentword-pairs it contains. The image patch is effectively converted toword-pairs that are joined by a “−” if they are horizontally adjacent(e.g., the−cat, in−the, the−hat, and is−back) and a “+” if they overlapvertically (e.g., the+in, cat+the, in+is, and the+back). The x-ylocations can be, for example, based on pixel counts in the x and yplane directions from some fixed point in document image (from theuppermost left corner or center of the document). Note that thehorizontally adjacent pairs in the example may occur frequently in manyother text passages, while the vertically overlapping pairs will likelyoccur infrequently in other text passages. Other geometric relationshipsbetween image features could be similarly encoded, such as SW-NEadjacency with a “/” between words, NW-SE adjacency with “\”, etc. Also,“features” could be generalized to word bounding boxes (or other featurebounding boxes) that could be encoded with arbitrary but consistentstrings. For example, a bounding box that is four times as long as it ishigh with a ragged upper contour but smooth lower contour could berepresented by the string “4rusl”. In addition, geometric relationshipscould be generalized to arbitrary angles and distance between features.For example, two words with the “4rusl” description that are NW-SEadjacent but separated by two word-heights could be represented“4rusl\\4rusl.” Numerous encoding schemes will be apparent in light ofthis disclosure. Furthermore, note that numbers, Boolean values,geometric shapes, and other such document features could be used insteadof word-pairs to ID a patch.

FIG. 34C illustrates an example index table organization in accordancewith one embodiment of the invention. As can be seen, the MMR indextable includes an inverted term index table 3422 and a document indextable 3424. Each unique term or feature (e.g., key 3421) points to alocation in the term index table 3422 that holds a functional value ofthe feature (e.g., key x) that points to a list of records 3423 (e.g.,Rec#1, Rec#2, etc), and each record identifies a candidate region on apage within a document, as will be discussed in turn. In one example,key and the functional value of the key (key x) are the same. In anotherexample a hash function is applied to key and the output of the functionis key x.

Given a list of query terms, every record indexed by the key isexamined, and the region most consistent with all query terms isidentified. If the region contains a sufficiently high matching score(e.g., based on a pre-defined matching threshold), the hypothesis isconfirmed. Otherwise, matching is declared to fail and no region isreturned. In this example embodiment, the keys are word-pairs separatedby either a “−” or a “+” as previously described (e.g., “the−cat” or“cat+the”). This technique of incorporating the geometric relationshipin the key itself allows use of conventional text search technology fora two-dimensional geometric query.

Thus, the index table organization transforms the features detected inan image patch into textual terms that represent both the featuresthemselves and the geometric relationship between them. This allowsutilization of conventional text indexing and search methods. Forexample, the vertically adjacent terms “cat” and “the” are representedby the symbol “cat+the” which can be referred to as a “query term” aswill be apparent in light of this disclosure. The utilization ofconventional text search data structures and methodologies facilitategrafting of MMR techniques described herein on top of Internet textsearch systems (e.g., Google, Yahoo, Microsoft, etc).

In the inverted term index table 3422 of this example embodiment, eachrecord identifies a candidate region on a page within a document usingsix parameters: document identification (DocID), page number (PG), x/yoffset (X and Y, respectively), and width and height of rectangular zone(W and H, respectively). The DocID is a unique string generated based onthe timestamp (or other metadata) when a document is printed. But it canbe any string combining device ID and person ID. In any case, documentsare identified by unique DocIDs, and have records that are stored in thedocument index table. Page number is the pagination corresponding to thepaper output, and starts at 1. A rectangular region is parameterized bythe X-Y coordinates of the upper-left corner, as well as the width andheight of the bounding box in normalized coordinate system. Numerousinner-document location/coordinate schemes will be apparent in light ofthis disclosure, and the present invention is not intended to be limitedany particular one.

An example record structure configured in accordance with one embodimentof the present invention uses a 24-bit DocID and an 8-bit page number,allowing up to 16 million documents and 4 billion pages. One unsignedbyte for each X and Y offset of the bounding box provide a spatialresolution of 30 dpi horizontal and 23 dpi vertical (assuming an 8.5″ by11″ page, although other page sizes and/or spatial resolutions can beused). Similar treatment for the width and height of the bounding box(e.g., one unsigned byte for each W and H) allows representation of aregion as small as a period or the dot on an “i”, or as large as anentire page (e.g., 8.5″ by 11″ or other). Therefore, eight bytes perrecord (3 bytes for DocID, 1 byte for PG, 1 byte for X, 1 byte for Y, 1byte for W, and 1 byte for H is a total of 8 bytes) can accommodate alarge number of regions.

The document index table 3424 includes relevant information about eachdocument. In one particular embodiment, this information includes thedocument-related fields in the XML file, including print resolution,print date, paper size, shadow file name, page image location, etc.Since print coordinates are converted to a normalized coordinate systemwhen indexing a document, computing search hypotheses does not involvethis table. Thus, document index table 3424 is only consulted formatched candidate regions. However, this decision does imply some lossof information in the index because the normalized coordinate is usuallyat a lower resolution than the print resolution. Alternative embodimentsmay use the document index table 3424 (or a higher resolution for thenormalized coordinate) when computing search hypotheses, if so desired.

Thus, the index table module 3404 operates to effectively provide animage index that enables content-based retrieval of objects (e.g.,document pages) and x-y locations within those objects where a givenimage query occurs. The combination of such an image index andrelational database 3408 allows for the location of objects that matchan image patch and characteristics of the patch (e.g., such as the“actions” attached to the patch, or bar codes that can be scanned tocause retrieval of other content related to the patch). The relationaldatabase 3408 also provides a means for “reverse links” from a patch tothe features in the index table for other patches in the document.Reverse links provide a way to find the features a recognition algorithmwould expect to see as it moves from one part of a document image toanother, which may significantly improve the performance of thefront-end image analysis algorithms in an MMR system as discussedherein.

Feedback-Directed Feature Search

The x-y coordinates of the image patch (e.g., x-y coordinates for thecenter of the image patch) as well as the identification of the documentand page can also be input to the feedback-directed feature searchmodule 3418. The feedback-directed feature search module 3418 searchesthe term index table 3422 for records 3423 that occur within a givendistance from the center of the image patch. This search can befacilitated, for example, by storing the records 3423 for each DocID-PGcombination in contiguous blocks of memory sorted in order of X or Yvalue. A lookup is performed by binary search for a given value (X or Ydepending on how the data was sorted when stored) and serially searchingfrom that location for all the records with a given X and Y value.Typically, this would include x-y coordinates in an M-inch ring aroundthe outside of a patch that measures W inches wide and H inches high inthe given document and page. Records that occur in this ring are locatedand their keys or features 3421 are located by tracing back pointers.The list of features and their x-y locations in the ring are reported asshown at 3417 of FIG. 34A. The values of W, H, and M shown at 3415 canbe set dynamically by the recognition system based on the size of theinput image so that the features 3417 are outside the input image patch.

Such characteristics of the image database system 3400 are useful, forexample, for disambiguating multiple hypotheses. If the database system3400 reports more than one document could match the input image patch,the features in the rings around the patches would allow the recognitionsystem (e.g., fingerprint matching module 226 or other suitablerecognition system) to decide which document best matches the documentthe user is holding by directing the user to slightly move the imagecapture device in the direction that would disambiguate the decision.For example (assume OCR-based features are used, although the conceptextends to any geometrically indexed feature set), an image patch indocument A might be directly below the word-pair “blue-xylophone.” Theimage patch in document B might be directly below the word-pair“blue-thunderbird.” The database system 3400 would report the expectedlocations of these features and the recognition system could instructthe user (e.g., via a user interface) to move the camera up by theamount indicated by the difference in y coordinates of the features andtop of the patch. The recognition system could compute the features inthat difference area and use the features from documents A and B todetermine which matches best. For example, the recognition system couldpost-process the OCR results from the difference area with the“dictionary” of features comprised of (xylophone, thunderbird). The wordthat best matches the OCR results corresponds to the document that bestmatches the input image. Examples of post-processing algorithms includecommonly known spelling correction techniques (such as those used byword processor and email applications).

As this example illustrates, the database system 3400 design allows therecognition system to disambiguate multiple candidates in an efficientmanner by matching feature descriptions in a way that avoids the need todo further database accesses. An alternative solution would be toprocess each image independently.

Dynamic Pristine Image Generation

The x-y coordinates for the location the image patch (e.g., x-ycoordinates for the center of the image patch) as well as theidentification of the document and page can also be input to therelational database 3408 where they can be used to retrieve the storedelectronic original for that document and page. That document can thenbe rendered by the document rendering application module 3414 as abitmap image. Also, an additional “box size” value provided by module3414 is used by the sub-image extraction module 3416 to extract aportion of the bitmap around the center. This bitmap is a “pristine”representation for the expected appearance of the image patch and itcontains an exact representation for all features that should be presentin the input image. The pristine patch can then be returned as a patchcharacteristic 3412. This solution overcomes the excessive storagerequired of prior techniques that store image bitmaps by storing acompact non-image representation that can subsequently be converted tobitmap data on demand.

Such as storage scheme is advantageous since it enables the use of ahypothesize-and-test recognition strategy in which a featurerepresentation extracted from an image is used to retrieve a set ofcandidates that is disambiguated by a detailed feature analysis. Often,it is not possible to predict the features that will optimallydisambiguate an arbitrary set of candidates and it is desirable thatthis be determined from the original images of those candidates. Forexample, an image of the word-pair “the cat” could be located in twodatabase documents, one of which was originally printed in a Times Romanfont and the other in a Helvetica font. Simply determining whether theinput image contains one of these fonts would identify the correctlymatching database document. Comparing the pristine patches for thosedocuments to the input image patch with a template matching comparisonmetric like the Euclidean distance would identify the correct candidate.

An example includes a relational database 3408 that stores MicrosoftWord “.doc” files (a similar methodology works for other documentformats such as postscript, PCL, pdf., or Microsoft's XML paperspecification XPS, or other such formats that can be converted to abitmap by a rendering application such as ghostscript or in the case ofXPS, Microsoft's Internet Explorer with the WinFX components installed).Given the identification for a document, page, x-y location, boxdimensions, and system parameters that indicate the preferred resolutionis 600 dots per inch (dpi), the Word application can be invoked togenerate a bitmap image. This will provide a bitmap with 6600 rows and5100 columns. Additional parameters x=3″, y=3″, height=1″, and width=1″indicate the database should return a patch 600 pixels high and widethat is centered at a point 1800 pixels in x and y away from the topleft corner of the page.

Multiple Databases

When multiple database systems 3400 are used, each of which may containdifferent document collections, pristine patches can be used todetermine whether two databases return the same document or whichdatabase returned the candidate that better matches the input.

If two databases return the same document, possibly with differentidentifiers 3410 (i.e., it is not apparent the original documents arethe same since they were separately entered in different databases) andcharacteristics 3412, the pristine patches will be almost exactly thesame. This can be determined by comparing the pristine patches to oneanother, for example, with a Hamming distance that counts the number ofpixels that are different. The Hamming distance will be zero if theoriginal documents are exactly the same pixel-for-pixel. The Hammingdistance will be slightly greater than zero if the patches are slightlydifferent as might be caused by minor font differences. This can cause a“halo” effect around the edges of characters when the image differencein the Hamming operator is computed. Font differences like this can becaused by different versions of the original rendering application,different versions of the operating system on the server that runs thedatabase, different printer drivers, or different font collections.

The pristine patch comparison algorithm can be performed on patches frommore than one x-y location in two documents. They should all be thesame, but a sampling procedure like this would allow for redundancy thatcould overcome rendering differences between database systems. Forexample, one font might appear radically different when rendered on thetwo systems but another font might be exactly the same.

If two or more databases return different documents as their best matchfor the input image, the pristine patches could be compared to the inputimage by a pixel based comparison metric such as Hamming distance todetermine which is correct.

An alternative strategy for comparing results from more than onedatabase is to compare the contents of accumulator arrays that measurethe geometric distribution of features in the documents reported by eachdatabase. It is desirable that this accumulator be provided directly bythe database to avoid the need to perform a separate lookup of theoriginal feature set. Also, this accumulator should be independent ofthe contents of the database system 3400. In the embodiment shown inFIG. 34A, an activity array 3420 is exported. Two Activity arrays can becompared by measuring the internal distribution of their values.

In more detail, if two or more databases return the same document,possibly with different identifiers 3410 (i.e., it's not apparent theoriginal documents are the same since they were separately entered indifferent databases) and characteristics 3412, the activity arrays 3420from each database will be almost exactly the same. This can bedetermined by comparing the arrays to one another, for example, with aHamming distance that counts the number of pixels that are different.The Hamming distance will be zero if the original documents are exactlythe same.

If two or more databases return different documents as their best matchfor the input features, their activity arrays 3420 can be compared todetermine which document “best” matches the input image. An Activityarray that correctly matches an image patch will contain a cluster ofhigh values approximately centered on the location where the patchoccurs. An Activity array that incorrectly matches an image patch willcontain randomly distributed values. There are many well knownstrategies for measuring dispersion or the randomness of an image, suchas entropy. Such algorithms can be applied to an activity array 3420 toobtain a measure that indicates the presence of a cluster. For example,the entropy of an activity array 3420 that contains a clustercorresponding to an image patch will be significantly different from theentropy of an activity array 3420 whose values are randomly distributed.

Further, it is noted that an individual client 106 might at any timehave access to multiple databases 3400 whose contents are notnecessarily in conflict with one another. For example, a corporationmight have both publicly accessible patches and ones private to thecorporation that each refer to a single document. In such cases, aclient device 106 would maintain a list of databases D1, D2, D3 . . . ,which are consulted in order, and produce combined activity arrays 3420and identifiers 3410 into a unified display for the user. A given clientdevice 106 might display the patches available from all databases, orallow a user to choose a subset of the databases (only D1, D3, and D7,for example) and only show patches from those databases. Databases mightbe added to the list by subscribing to a service, or be made availablewirelessly when a client device 106 is in a certain location, or becausethe database is one of several which have been loaded onto client device106, or because a certain user has been authenticated to be currentlyusing the device, or even because the device is operating in a certainmode. For example, some databases might be available because aparticular client device has its audio speaker turned on or off, orbecause a peripheral device like a video projector is currently attachedto the client.

Actions

With further reference to FIG. 34A, the MMR database 3400 receives anaction together with a set of features from the MMR feature extractionmodule 3402. Actions specify commands and parameters. In such anembodiment, the command and its parameters determine the patchcharacteristics that are returned 3412. Actions are received in a formatincluding, for example, http that can be easily translated into text.

The action processor 3413 receives the identification for a document,page and x-y location within a page determined by the evidenceaccumulation module 3406. It also receives a command and its parameters.The action processor 3413 is programmed or otherwise configured totransform the command into instructions that either retrieve or storedata using the relational database 3408 at a location that correspondswith the given document, page and x-y location.

In one such embodiment, commands include: RETRIEVE, INSERT_TO <DATA>,RETRIEVE_TEXT <RADIUS>, TRANSFER <AMOUNT>, PURCHASE, PRISTINE_PATCH<RADIUS [DOCID PAGEID X Y DPI]>, and ACCESS_DATABASE <DBID>. Each willnow be discussed in turn.

RETRIEVE—retrieve data linked to the x-y location in the given documentpage. The action processor 3413 transforms the RETRIEVE command to therelational database query that retrieves data that might be storednearby this x-y location. This can require the issuance of more than onedatabase query to search the area surrounding the x-y location. Theretrieved data is output as patch characteristics 3412. An exampleapplication of the RETRIEVE command is a multimedia browsing applicationthat retrieves video clips or dynamic information objects (e.g.,electronic addresses where current information can be retrieved). Theretrieved data can include menus that specify subsequent steps to beperformed on the MMR device. It could also be static data that could bedisplayed on a phone (or other display device) such as JPEG images orvideo clips. Parameters can be provided to the RETRIEVE command thatdetermine the area searched for patch characteristics

INSERT_TO <DATA>—insert <DATA> at the x-y location specified by theimage patch. The action processor 3413 transforms the INSERT_TO commandto an instruction for the relational database that adds data to thespecified x-y location. An acknowledgement of the successful completionof the INSERT_TO command is returned as patch characteristics 3412. Anexample application of the INSERT_TO command is a software applicationon the MMR device that allows a user to attach data to an arbitrary x-ylocation in a passage of text. The data can be static multimedia such asJPEG images, video clips, or audio files, but it can also be arbitraryelectronic data such as menus that specify actions associated with thegiven location.

RETRIEVE_TEXT <RADIUS>—retrieve text within <RADIUS> of the x-y locationdetermined by the image patch. The <RADIUS> can be specified, forexample, as a number of pixels in image space or it can be specified asa number of characters of words around the x-y location determined bythe evidence accumulation module 3406. <RADIUS> can also refer to parsedtext objects. In this particular embodiment, the action processor 3413transforms the RETRIEVE_TEXT command into a relational database querythat retrieves the appropriate text. If the <RADIUS> specifies parsedtext objects, the Action Processor only returns parsed text objects. Ifa parsed text object is not located nearby the specified x-y location,the Action Processor returns a null indication. In an alternateembodiment, the Action Processor calls the Feedback-Directed FeaturesSearch module to retrieve the text that occurs within a radius of thegiven x-y location. The text string is returned as patch characteristics3412. Optional data associated with each word in the text stringincludes its x-y bounding box in the original document. An exampleapplication of the RETRIEVE_TEXT command is choosing text phrases from aprinted document for inclusion in another document. This could be used,for example, for composing a presentation file (e.g., in PowerPointformat) on the MMR system.

TRANSFER<AMOUNT>—retrieve the entire document and some of the datalinked to it in a form that could be loaded into another database.<AMOUNT> specifies the number and type of data that is retrieved. If<AMOUNT> is ALL, the action processor 3413 issues a command to thedatabase 3408 that retrieves all the data associated with a document.Examples of such a command include DUMP or Unix TAR. If <AMOUNT> isSOURCE, the original source file for the document is retrieved. Forexample, this could retrieve the Word file for a printed document. If<AMOUNT> is BITMAP the JPEG-compressed version (or other commonly usedformats) of the bitmap for the printed document is retrieved. If<AMOUNT> is PDF, the PDF representation for the document is retrieved.The retrieved data is output as patch characteristics 3412 in a formatknown to the calling application by virtue of the command name. Anexample application of the TRANSFER command is a “document grabber” thatallows a user to transfer the PDF representation for a document to anMMR device by imaging a small area of text.

PURCHASE—retrieve a product specification linked to an x-y location in adocument. The action processor 3413 first performs a series of one ormore RETRIEVE commands to obtain product specifications nearby a givenx-y location. A product specification includes, for example, a vendorname, identification for a product (e.g., stock number), and electronicaddress for the vendor. Product specifications are retrieved inpreference to other data types that might be located nearby. Forexample, if a jpeg is stored at the x-y location determined by the imagepatch, the next closest product specification is retrieved instead. Theretrieved product specification is output as patch characteristics 3412.An example application of the PURCHASE command is associated withadvertising in a printed document. A software application on the MMRdevice receives the product specification associated with theadvertising and adds the user's personal identifying information (e.g.,name, shipping address, credit card number, etc.) before sending it tothe specified vendor at the specified electronic address.

PRISTINE_PATCH <RADIUS [DOCID PAGEID X Y DPI]>—retrieve an electronicrepresentation for the specified document and extract an image patchcentered at x-y with radius RADIUS. RADIUS can specify a circular radiusbut it can also specify a rectangular patch (e.g., 2 inches high by 3inches wide). It can also specify the entire document page. The (DocID,PG, x, y) information can be supplied explicitly as part of the actionor it could be derived from an image of a text patch. The actionprocessor 3413 retrieves an original representation for a document fromthe relational database 3408. That representation can be a bitmap but itcan also be a renderable electronic document. The originalrepresentation is passed to the document rendering application 3414where it is converted to a bitmap (with resolution provided in parameterDPI as dots per inch) and then provided to sub-image extraction 3416where the desired patch is extracted. The patch image is returned aspatch characteristics 3412.

ACCESS_DATABASE <DBID>—add the database 3400 to the database list ofclient 106. Client can now consult this database 300 in addition to anyexisting databases currently in the list. DBID specifies either a fileor remote network reference to the specified database.

Index Table Generation Methodology

FIG. 35 illustrates a method 3500 for generating an MMR index table inaccordance with an embodiment of the present invention. The method canbe carried out, for example, by database system 3400 of FIG. 34A. In onesuch embodiment, the MMR index table is generated, for example, by theMMR index table module 3404 (or some other dedicated module) from ascanned or printed document. The generating module can be implemented insoftware, hardware (e.g., gate-level logic), firmware (e.g., amicrocontroller configured with embedded routines for carrying out themethod, or some combination thereof, just as other modules describedherein.

The method includes receiving 3510 a paper document. The paper documentcan be any document, such as a memo having any number of pages (e.g.,work-related, personal letter), a product label (e.g., canned goods,medicine, boxed electronic device), a product specification (e.g., snowblower, computer system, manufacturing system), a product brochure oradvertising materials (e.g., automobile, boat, vacation resort), servicedescription materials (e.g., Internet service providers, cleaningservices), one or more pages from a book, magazine or other suchpublication, pages printed from a website, hand-written notes, notescaptured and printed from a white-board, or pages printed from anyprocessing system (e.g., desktop or portable computer, camera,smartphone, remote terminal).

The method continues with generating 3512 an electronic representationof the paper document, the representation including x-y locations offeatures shown in the document. The target features can be, forinstance, individual words, letters, and/or characters within thedocument. For example, if the original document is scanned, it is firstOCR'd and the words (or other target feature) and their x-y locationsare extracted (e.g., by operation of document fingerprint matchingmodule 226′ of scanner 127). If the original document is printed, theindexing process receives a precise representation (e.g., by operationof print driver 316 of printer 116) in XML format of the font, pointsize, and x-y bounding box of every character (or other target feature).In this case, index table generation begins at step 3514 since anelectronic document is received with precisely identified x-y featurelocations (e.g., from print driver 316). Formats other than XML will beapparent in light of this disclosure. Electronic documents such asMicrosoft Word, Adobe Acrobat, and postscript can be entered in thedatabase by “printing” them to a print driver whose output is directedto a file so that paper is not necessarily generated. This triggers theproduction of the XML file structure shown below. In all cases, the XMLas well as the original document format (Word, Acrobat, postscript,etc.) are assigned an identifier (doc i for the ith document added tothe database) and stored in the relational database 3408 in a way thatenables their later retrieval by that identifier but also based on other“meta data” characteristics of the document including the time it wascaptured, the date printed, the application that triggered the print,the name of the output file, etc.

An example of the XML file structure is shown here: $docID.xml : <?xmlversion=“1.0” ?> <doclayout ID=“00001234”> <setup> <url>file url/path ornull if not known</url> <date>file printed date</date> <app>applicationthat triggered print</app> <text>$docID.txt</text> <prfile>name ofoutput file</prfile> <dpi>dpi of page for x, y coordinates, eg.600</dpi><width>in inch, like 8.5</width> <height>in inch, eg. 11.0</height><imagescale>0.1 is 1/10th scale of dpi</imagescale> </setup> <pageno=“1> <image>$docID_1.jpeg</image> <sequence box=“x y w h”> <text>thisstring of text</text> <font>any font info</font> <word box=“x y w h”><text>word text</text> <char box=“x y w h”>a</char> <char box=“x y wh”>b</char> <char>1 entry per char, in sequence</char> </word></sequence> </page> </doclayout>In one specific embodiment, a word may contain any characters from a-z,A-Z, 0-9, and any of @%$#; all else is a delimiter. The originaldescription of the .xml file can be created by print capture softwareused by the indexing process (e.g., which executes on a server, such asdatabase 320 server). The actual format is constantly evolving andcontains more elements, as new documents are acquired by the system.

The original sequence of text received by the print driver (e.g., printdriver 316) is preserved and a logical word structure is imposed basedon punctuation marks, except for “_@%$#”. Using the XML file as input,the index table module 3404 respects the page boundary, and first triesto group sequences into logical lines by checking the amount of verticaloverlap between two consecutive sequences. In one particular embodiment,the heuristic that a line break occurred is used if two sequencesoverlap by less than half of their average height. Such a heuristicworks well for typical text documents (e.g., Microsoft Word documents).For html pages with complex layout, additional geometrical analysis maybe needed. However, it is not necessary to extract perfect semanticdocument structures as long as consistent indexing terms can begenerated as by the querying process.

Based on the structure of the electronic representation of the paperdocument, the method continues with indexing 3514 the location of everytarget feature on every page of the paper document. In one particularembodiment, this step includes indexing the location of every pair ofhorizontally and vertically adjacent words on every page of the paperdocument. As previously explained, horizontally adjacent words are pairsof neighboring words within a line. Vertically adjacent words are wordsin neighboring lines that vertically align. Other multi-dimensionalaspects of the a page can be similarly exploited.

The method further includes storing 3516 patch characteristicsassociated with each target feature. In one particular embodiment, thepatch characteristics include actions attached to the patch, and arestored in a relational database. As previously explained, thecombination of such an image index and storage facility allows for thelocation of objects that match an image patch and characteristics of thepatch. The characteristics can be any data related to the path, such asmetadata. The characteristics can also include, for example, actionsthat will carry out a specific function, links that can be selected toprovide access to other content related to the patch, and/or bar codesthat can be scanned or otherwise processed to cause retrieval of othercontent related to the patch.

A more precise definition is given for the search term generation, whereonly a fragment of the line structure is observed. For horizontallyadjacent pairs, a query term is formed by concatenating the words with a“−” separator. Vertical pairs are concatenated using a “+”. The wordscan be used in their original form to preserve capitalization if sodesired (this creates more unique terms but also produces a larger indexwith additional query issues to consider such as case sensitivity). Theindexing scheme allows the same search strategy to be applied on eitherhorizontal or vertical word-pairs, or a combination of both. Thediscriminating power of terms is accounted for by the inverse documentfrequency for any of the cases.

Evidence Accumulation Methodology

FIG. 36 illustrates a method 3600 for computing a ranked set ofdocument, page, and location hypotheses for a target document, inaccordance with one embodiment of the present invention. The method canbe carried out, for example, by database system 3400 of FIG. 34A. In onesuch embodiment, the evidence accumulation module 3406 computeshypotheses using data from the index table module 3404 as previouslydiscussed.

The method begins with receiving 3610 a target document image, such asan image patch of a larger document image or an entire document image.The method continues with generating 3612 one or more query terms thatcapture two-dimensional relationships between objects in the targetdocument image. In one particular embodiment, the query terms aregenerated by a feature extraction process that produces horizontal andvertical word-pairs, as previously discussed with reference to FIG. 34B.However, any number of feature extraction processes as described hereincan be used to generate query terms that capture two-dimensionalrelationships between objects in the target image, as will be apparentin light of this disclosure. For instance, the same feature extractiontechniques used to build the index of method 3500 can be used togenerate the query terms, such as those discussed with reference to step3512 (generating an electronic representation of a paper document).Furthermore, note that the two-dimensional aspect of the query terms canbe applied to each query term individually (e.g., a single query termthat represents both horizontal and vertical objects in the targetdocument) or as a set of search terms (e.g., a first query term that isa horizontal word-pair and a second query term that is a verticalword-pair).

The method continues with looking-up 3614 each query term in a termindex table 3422 to retrieve a list of locations associated with eachquery term. For each location, the method continues with generating 3616a number of regions containing the location. After all queries areprocessed, the method further includes identifying 3618 a region that ismost consistent with all query terms. In one such embodiment, a scorefor every candidate region is incremented by a weight (e.g., based onhow consistent each region is with all query terms). The methodcontinues with determining 3620 if the identified region satisfies apre-defined matching criteria (e.g., based on a pre-defined matchingthreshold). If so, the method continues with confirming 3622 the regionas a match to the target document image (e.g., the page that most likelycontains the region can be accessed and otherwise used). Otherwise, themethod continues with rejecting 3624 the region.

Word-pairs are stored in the term index table 3422 with locations in a“normalized” coordinate space. This provides uniformity betweendifferent printer and scanner resolutions. In one particular embodiment,an 85×110 coordinate space is used for 8.5″ by 11″ pages. In such acase, every word-pair is identified by its location in this 85×110space.

To improve the efficiency of the search, a two-step process can beperformed. The first step includes locating the page that most likelycontains the input image patch. The second step includes calculating thex-y location within that page that is most likely the center of thepatch. Such an approach does introduce the possibility that the truebest match may be missed in the first step. However, with a sparseindexing space, such a possibility is rare. Thus, depending on the sizeof the index and desired performance, such an efficiency improvingtechnique can be employed.

In one such embodiment, the following algorithm is used to find the pagethat most likely contains the word-pairs detected in the input imagepatch. For each given word-pair wp idf = 1/log(2 + num_docs(wp)) Foreach (doc, page) at which wp occurred Accum[doc, page] += idf; end /*For each (doc, page) */ end /* For each wp */ (maxdoc, maxpage) = max(Accum[doc, page] ); if (Accum[ maxdoc, maxpage ] > thresh_page) return(maxdoc, maxpage);This technique adds the inverse document frequency (idf) for eachword-pair to an accumulator indexed by the documents and pages on whichit appears. num_docs(wp) returns the number of documents that containthe word pair wp. The accumulator is implemented by the evidenceaccumulation module 3406. If the maximum value in that accumulatorexceeds a threshold, it is output as the page that is the best match tothe patch. Thus, the algorithm operates to identify the page that bestmatches the word-pairs in the query. Alternatively, the Accum array canbe sorted and the top N pages reported as the “N best” pages that matchthe input document.

The following evidence accumulation algorithm accumulates evidence forthe location of the input image patch within a single page, inaccordance with one embodiment of the present invention. For each givenword-pair wp idf = 1/log(2 + num_docs(wp)) For each (x,y) at which wpoccurred (minx, maxx, miny, maxy) = extent(x,y); maxdist = maxdist(minx,maxx, miny, maxy); For i=miny to maxy do For j = minx to maxx donorm_dist = Norm_geometric_dist(i, j, x, y, maxdist) Activity [i,j] +=norm_dist; weight = idf * norm_dist; Accum2[i,j] += weight; end /* for j*/ end /* for I */ end /* For each (y,y) */ end /* For each */The algorithm operates to locate the cell in the 85×110 space that ismost likely the center of the input image patch. In the embodiment shownhere, the algorithm does this by adding a weight to the cells in a fixedarea around each word-pair (called a zone). The extent function is givenan x,y pair and it returns the minimum and maximum values for asurrounding fixed size region (1.5″ high and 2″ wide are typical). Theextent function takes care of boundary conditions and makes sure thevalues it returns do not fall outside the accumulator (i.e., less thanzero or greater than 85 in x or 110 in y). The maxdist function findsthe maximum Euclidean distance between two points in a bounding boxdescribed by the bounding box coordinates (minx, maxx, miny, maxy). Aweight is calculated for each cell within the zone that is determined byproduct of the inverse document frequency of the word-pair and thenormalized geometric distance between the cell and the center of thezone. This weights cells closer to the center higher than cells furtheraway. After every word-pair is processed by the algorithm, the Accum2array is searched for the cell with the maximum value. If that exceeds athreshold, its coordinates are reported as the location of the imagepatch. The Activity array stores the accumulated norm_dist values. Sincethey aren't scaled by idf, they don't take into account the number ofdocuments in a database that contain particular word pairs. However,they do provide a two-dimensional image representation for the x-ylocations that best match a given set of word pairs. Furthermore,entries in the Activity array are independent of the documents stored inthe database. This data structure, that's normally used internally, canbe exported 3420.

The normalized geometric distance is calculated as shown here, inaccordance with one embodiment of the present invention.Norm_geometric_dist(i, j, x, y, maxdist) begin d = sqrt( (i−x)² + (j−y)²); return( maxdist − d ); endThe Euclidean distance between the word-pair's location and the centerof the zone is calculated and the difference between this and themaximum distance that could have been calculated is returned.

After every word-pair is processed by the evidence accumulationalgorithm, the Accum2 array is searched for the cell with the maximumvalue. If that value exceeds a pre-defined threshold, its coordinatesare reported as the location of the center of the image patch.

MMR Printing Architecture

FIG. 37A illustrates a functional block diagram of MMR components inaccordance with one embodiment of the present invention. The primary MMRcomponents include a computer 3705 with an associated printer 116 and/ora shared document annotation (SDA) server 3755.

The computer 3705 is any standard desktop, laptop, or networkedcomputer, as is known in the art. In one embodiment, the computer is MMRcomputer 112 as described in reference to FIG. 1B. User printer 116 isany standard home, office, or commercial printer, as described herein.User printer 116 produces printed document 118, which is a paperdocument that is formed of one or more printed pages.

The SDA server 3755 is a standard networked or centralized computer thatholds information, applications, and/or a variety of files associatedwith a method of shared annotation. For example, shared annotationsassociated with web pages or other documents are stored at the SDAserver 3755. In this example, the annotations are data or interactionsused in MMR as described herein. The SDA server 3755 is accessible via anetwork connection according to one embodiment. In one embodiment, theSDA server 3755 is the networked media server 114 described in referenceto FIG. 1B.

The computer 3705 further comprises a variety of components, some or allof which are optional according to various embodiments. In oneembodiment, the computer 3705 comprises source files 3710, browser 3715,plug-in 3720, symbolic hotspot description 3725, modified files 3730,capture module 3735, page_desc.xml 3740, hotspot.xml 3745, data store3750, SDA server 3755, and MMR printer software 3760.

Source files 3710 are representative of any source files that are anelectronic representation of a document. Example source files 3710include hypertext markup language (HTML) files, Microsoft® Word® files,Microsoft® PowerPoint® files, simple text files, portable documentformat (PDF) files, and the like. As described herein, documentsreceived at browser 3715 originate from source files 3710 in manyinstances. In one embodiment, source files 3710 are equivalent to sourcefiles 310 as described in reference to FIG. 3.

Browser 3715 is an application that provides access to data that hasbeen associated with source files 3710. For example, the browser 3715may be used to retrieve web pages and/or documents from the source files3710. In one embodiment, browser 3715 is an SD browser 312, 314, asdescribed in reference to FIG. 3. In one embodiment, the browser 3715 isan Internet browser such as Internet Explorer.

Plug-in 3720 is a software application that provides an authoringfunction. Plug-in 3720 is a standalone software application or,alternatively, a plug-in running on browser 3715. In one embodiment,plug-in 3720 is a computer program that interacts with an application,such as browser 3715, to provide the specific functionality describedherein. The plug-in 3720 performs various transformations and othermodifications to documents or web pages displayed in the browser 3715according to various embodiments. For example, plug-in 3720 surroundshotspot designations with an individually distinguishable fiducial marksto create hotspots and returns “marked-up” versions of HTML files to thebrowser 3715, applies a transformation rule to a portion of a documentdisplayed in the browser 3715, and retrieves and/or receives sharedannotations to documents displayed in the browser 3715. In addition,plug-in 3720 may perform other functions, such as creating modifieddocuments and creating symbolic hotspot descriptions 3725 as describedherein. Plug-in 3720, in reference to capture module 3735, facilitatesthe methods described in reference to FIGS. 38, 44, 45, 48, and 50A-B.

Symbolic hotspot description 3725 is a file that identifies a hotspotwithin a document. Symbolic hotspot description 3725 identifies thehotspot number and content. In this example, symbolic hotspotdescription 3725 is stored to data store 3750. An example of a symbolichotspot description is shown in greater detail in FIG. 41.

Modified files 3730 are documents and web pages created as a result ofthe modifications and transformations of source files 3710 by plug-in3720. For example, a marked-up HTML file as noted above is an example ofa modified file 3730. Modified files 3730 are returned to browser 3715for display to the user, in certain instances as will be apparent inlight of this disclosure.

Capture module 3735 is a software application that performs a featureextraction and/or coordinate capture on the printed representation ofdocuments, so that the layout of characters and graphics on the printedpages can be retrieved. The layout, i.e., the two-dimensionalarrangement of text on the printed page, may be captured automaticallyat the time of printing. For example, capture module 3735 executes allthe text and drawing print commands and, in addition, intercepts andrecords the x-y coordinates and other characteristics of every characterand/or image in the printed representation. According to one embodiment,capture module 3735 is a Printcapture DLL as described herein, aforwarding Dynamically Linked Library (DLL) that allows addition ormodification of the functionality of an existing DLL. A more detaileddescription of the functionality of capture module 3735 is described inreference to FIG. 44.

Those skilled in the art will recognize that the capture module 3735 iscoupled to the output of browser 3715 for capture of data.Alternatively, the functions of capture module 3735 may be implementeddirectly within a printer driver. In one embodiment, capture module 3735is equivalent to PD capture module 318, as described in reference toFIG. 3.

Page_desc.xml 3740 is an extensible markup language (“XML”) file towhich text-related output is written for function calls processed bycapture module 3735 that are text related. The page_desc.xml 3740includes coordinate information for a document for all printed text byword and by character, as well as hotspot information, printer portname, browser name, date and time of printing, and dots per inch (dpi)and resolution (res) information. page_desc.xml 3740 is stored, e.g., indata store 3750. Data store 3750 is equivalent to MMR database 3400described with reference to FIG. 34A. FIGS. 42A-B illustrate in greaterdetail an example of a page_desc.xml 3740 for an HTML file.

hotspot.xml 3745 is an XML file that is created when a document isprinted (e.g., by operation of print driver 316, as previouslydiscussed). hotspot.xml is the result of merging symbolic hotspotdescription 3725 and page_desc.xml 3740. hotspot.xml includes hotspotidentifier information such as hotspot number, coordinate information,dimension information, and the content of the hotspot. An example of ahotspot.xml file is illustrated in FIG. 43.

Data store 3750 is any database known in the art for storing files,modified for use with the methods described herein. For example,according to one embodiment data store 3750 stores source files 3710,symbolic hotspot description 3725, page_desc.xml 3740, rendered pagelayouts, shared annotations, imaged documents, hot spot definitions, andfeature representations. In one embodiment, data store 3750 isequivalent to document event database 320 as described with reference toFIG. 3 and to database system 3400 as described with reference to FIG.34A.

MMR printing software 3760 is the software that facilitates the MMRprinting operations described herein, for example as performed by thecomponents of computer 3705 as previously described. MMR printingsoftware 3760 is described below in greater detail with reference toFIG. 37B.

FIG. 37B illustrates a set of software components included MMR printingsoftware 3760 in accordance with one embodiment of the invention. Itshould be understood that all or some of the MMR printing software 3760may be included in the computer 112, 905, the capture device 106, thenetworked media server 114 and other servers as described herein. Whilethe MMR printing software 3760 will now be described as including thesedifferent components, those skilled in the art will recognize that theMMR printing software 3760 could have any number of these componentsfrom one to all of them. The MMR printing software 3760 includes aconversion module 3765, an embed module 3768, a parse module 3770, atransform module 3775, a feature extraction module 3778, an annotationmodule 3780, a hotspot module 3785, a render/display module 3790, and astorage module 3795.

Conversion module 3765 enables conversion of a source document into animaged document from which a feature representation can be extracted,and is one means for so doing.

Embed module 3768 enables embedding of marks corresponding to adesignation for a hot spot in an electronic document, and is one meansfor so doing. In one particular embodiment, the embedded marks indicatea beginning point for the hot spot and an ending point for the hotspot.Alternatively, a pre-define area around an embodiment mark can be usedto identify a hot spot in an electronic document. Various such markingschemes can be used.

Parse module 3770 enables parsing an electronic document (that has beensent to the printer) for a mark indicating a beginning point for ahotspot, and is one means for so doing.

Transformation module 3775 enables application of a transformation ruleto a portion of an electronic document, and is one means for so doing.In one particular embodiment, the portion is a stream of charactersbetween a mark indicating a beginning point for a hotspot and a markindicating an ending point for the hotspot.

Feature extraction module 3778 enables the extraction of features andcapture of coordinates corresponding to a printed representation of adocument and a hot spot, and is one means for so doing. Coordinatecapture includes tapping print commands using a forwarding dynamicallylinked library and parsing the printed representation for a subset ofthe coordinates corresponding to a hot spot or transformed characters.Feature extraction module 3778 enables the functionality of capturemodule 3735 according to one embodiment.

Annotation module 3780 enables receiving shared annotations and theiraccompanying designations of portions of a document associated with theshared annotations, and is one means for so doing. Receiving sharedannotations includes receiving annotations from end users and from a SDAserver.

Hotspot module 3785 enables association of one or more clips with one ormore hotspots, and is one means for so doing. Hotspot module 3785 alsoenables formulation of a hotspot definition by first designating alocation for a hotspot within a document and defining a clip toassociate with the hotspot.

Render/display module 3790 enables a document or a printedrepresentation of a document to be rendered or displayed, and is onemeans for so doing.

Storage module 3795 enables storage of various files, including a pagelayout, an imaged document, a hotspot definition, and a featurerepresentation, and is one means for so doing.

The software portions 3765-3795 need not be discrete software modules.The software configuration shown is meant only by way of example; otherconfigurations are contemplated by and within the scope of the presentinvention, as will be apparent in light of this disclosure.

Embedding a Hot Spot in a Document

FIG. 38 illustrates a flowchart of a method of embedding a hot spot in adocument in accordance with one embodiment of the present invention.

According to the method, marks are embedded 3810 in a documentcorresponding to a designation for a hotspot within the document. In oneembodiment, a document including a hotspot designation location isreceived for display in a browser, e.g., a document is received atbrowser 3715 from source files 3710. A hot spot includes some text orother document objects such as graphics or photos, as well as electronicdata. The electronic data can include multimedia such as audio or video,or it can be a set of steps that will be performed on a capture devicewhen the hot spot is accessed. For example, if the document is aHyperText Markup Language (HTML) file, the browser 3715 may be InternetExplorer, and the designations may be Uniform Resource Locators (URLs)within the HTML file. FIG. 39A illustrates an example of such an HTMLfile 3910 with a URL 3920. FIG. 40A illustrates the text of HTML file3910 of FIG. 39A as displayed in a browser 4010, e.g., InternetExplorer.

To embed 3810 the marks, a plug-in 3720 to the browser 3715 surroundseach hotspot designation location with an individually distinguishablefiducial mark to create the hotspot. In one embodiment, the plug-in 3720modifies the document displayed in the browser 3715, e.g., HTMLdisplayed in Internet Explorer continuing the example above, and insertsmarks, or tags, that bracket the hotspot designation location (e.g.,URL). The marks are imperceptible to the end user viewing the documenteither in the browser 3715 or a printed version of the document, but canbe detected in print commands. In this example a new font, referred toherein as MMR Courier New, is used for adding the beginning and endingfiducial marks. In MMR Courier New font, the typical glyph or dotpattern representation for the characters “b,” “e,” and the digits arerepresented by an empty space.

Referring again to the example HTML page shown in FIGS. 39A and 40A, theplug-in 3720 embeds 3810 the fiducial mark “b0” at the beginning of theURL (“here”) and the fiducial mark “e0” at the end of the URL, toindicate the hotspot with identifier “0.” Since the b, e, and digitcharacters are shown as spaces, the user sees little or no change in theappearance of the document. In addition, the plug-in 3720 creates asymbolic hotspot description 3725 indicating these marks, as shown inFIG. 41. The symbolic hotspot description 3725 identifies the hotspotnumber as zero 4120, which corresponds to the 0 in the “b0” and “e0”fiducial markers. In this example, the symbolic hotspot description 3725is stored, e.g., to data store 3750.

The plug-in 3720 returns a “marked-up” version of the HTML 3950 to thebrowser 3715, as shown in FIG. 39B. The marked-up HTML 3950 surroundsthe fiducial marks with span tags 3960 that change the font to 1-pointMMR Courier New. Since the b, e, and digit characters are shown asspaces, the user sees little or no change in the appearance of thedocument. The marked-up HTML 3950 is an example of a modified file 3730.This example uses a single page model for simplicity, however, multiplepage models use the same parameters. For example, if a hotspot spans apage boundary, it would have fiducial marks corresponding to each pagelocation, the hotspot identifier for each is the same.

Next, in response to a print command, coordinates corresponding theprinted representation and the hot spot are captured 3820. In oneembodiment, a capture module 3735 “taps” text and drawing commandswithin a print command. The capture module 3735 executes all the textand drawing commands and, in addition, intercepts and records the x-ycoordinates and other characteristics of every character and/or image inthe printed representation. In this example, the capture module 3735references the Device Context (DC) for the printed representation, whichis a handle to the structure of the printed representation that definesthe attributes of text and/or images to be output dependent upon theoutput format (i.e., printer, window, file format, memory buffer, etc.).In the process of capturing 3820 the coordinates for the printedrepresentation, the hotspots are easily identified using the embeddedfiducial marks in the HTML. For example, when the begin mark isencountered, the x-y location if recorded of all characters until theend mark is found.

According to one embodiment, the capture module 3735 is a forwardingDLL, referred to herein as “Printcapture DLL,” which allows addition ormodification of the functionality of an existing DLL. Forwarding DLLsappear to the client exactly as the original DLL, however, additionalcode (a “tap”) is added to some or all of the functions before the callis forwarded to the target (original) DLL. In this example, thePrintcapture DLL is a forwarding DLL for the Windows Graphics DeviceInterface (Windows GDI) DLL gdi32.dll. gdi32.dll has over 600 exportedfunctions, all of which need to be forwarded. The Printcapture DLL,referenced herein as gdi32_mmr.dll, allows the client to captureprintouts from any Windows application that uses the DLL gdi32.dll fordrawing, and it only needs to execute on the local computer, even ifprinting to a remote server.

According to one embodiment, gdi32_mmr.dll is renamed as gdi32.dll andcopied into C:\Windows\system32, causing it to monitor printing fromnearly every Windows application. According to another embodiment,gdi32_mmr.dll is named gdi32.dll and copied it into the home directoryof the application for which printing is monitored. For example,C:\Program Files\Internet Explorer for monitoring Internet Explorer onWindows XP. In this example, only this application (e.g., InternetExplorer) will automatically call the functions in the Printcapture DLL.

FIG. 44 illustrates a flowchart of the process used by a forwarding DLLin accordance with one embodiment of the present invention. ThePrintcapture DLL gdi32_mmr.dll first receives 4405 a function calldirected to gdi32.dll. In one embodiment, gdi32_mmr.dll receives allfunction calls directed to gdi32.dll. gdi32.dll monitors approximately200 of about 600 total function calls, which are for functions thataffect the appearance of a printed page in some way. Thus, thePrintcapture DLL next determines 4410 whether the received call is amonitored function call. If the received call is not a monitoredfunction call, the call bypasses steps 4415 through 4435, and isforwarded 4440 to gdi32.dll.

If it is a monitored function call, the method next determines 4415whether the function call specifies a “new” printer device context (DC),i.e., a printer DC that has not been previously received. This isdetermined by checking the printer DC against an internal DC table. A DCencapsulates a target for drawing (which could be a printer, a memorybuffer, etc.), as previously noted, as well as drawing settings likefont, color, etc. All drawing operations (e.g., LineTo( ), DrawText( ),etc) are performed upon a DC. If the printer DC is not new, then amemory buffer already exists that corresponds with the printer DC, andstep 4420 is skipped. If the printer DC is new, a memory buffer DC iscreated 4420 that corresponds with the new printer DC. This memorybuffer DC mirrors the appearance of the printed page, and in thisexample is equivalent to the printed representation referenced above.Thus, when a printer DC is added to the internal DC table, a memorybuffer DC (and memory buffer) of the same dimensions is created andassociated with the printer DC in the internal DC table.

gdi32_mmr.dll next determines 4425 whether the call is a text-relatedfunction call. Approximately 12 of the 200 monitored gdi32.dll calls aretext-related. If it is not, step 4430 is skipped. If the function callis text-related, the text-related output is written 4430 to an xml file,referred to herein as page_desc.xml 3740, as shown in FIG. 37A.page_desc.xml 3740 is stored, e.g., in data store 3750.

FIGS. 42A and 42B show an example page_desc.xml 3740 for the HTML file3910 example discussed in reference to FIGS. 39A and 40A. Thepage_desc.xml 3740 includes coordinate information for all printed textby word 4210 (e.g., Get), by x, y, width, and height, and by character4220 (e.g., G). All coordinates are in dots, which are the printerequivalent of pixels, relative to the upper-left-corner of the page,unless otherwise noted. The page_desc.xml 3740 also includes the hotspotinformation, such as the beginning mark 4230 and the ending mark 4240,in the form of a “sequence.” For a hotspot that spans a page boundary(e.g., of page N to page N+1), it shows up on both pages (N and N+1);the hotspot identifier in both cases is the same. In addition, otherimportant information is included in page_desc.xml 3740, such as theprinter port name 4250, which can have a significant effect on the .xmland .jpeg files produced, the browser 3715 (or application) name 4260,and the date and time of printing 4270, as well as dots per inch (dpi)and resolution (res) for the page 4280 and the printable region 4290.

Referring again to FIG. 44, following the determination that the call isnot text related, or following writing 4430 the text-related output topage_desc.xml 3740, gdi32_mmr.dll executes 4435 the function call on thememory buffer for the DC. This step 4435 provides for the output to theprinter to also get output to a memory buffer on the local computer.Then, when the page is incremented, the contents of the memory bufferare compressed and written out in JPEG and PNG format. The function callthen is forwarded 4440 to gdi32.dll, which executes it as it normallywould.

Referring again to FIG. 38, a page layout is rendered 3830 comprisingthe printed representation including the hot spot. In one embodiment,the rendering 3830 includes printing the document. FIG. 40B illustratesan example of a printed version 4011 of the HTML file 3910 of FIGS. 39Aand 40A. Note that the fiducial marks are not visibly perceptible to theend user. The rendered layout is saved, e.g., to data store 3750.

According to one embodiment, the Printcapture DLL merges the data in thesymbolic hotspot description 3725 and the page_desc.xml 3740, e.g., asshown in FIGS. 42A-B, into a hotspot.xml 3745, as shown in FIG. 43. Inthis example, hotspot.xml 3745 is created when the document is printed.The example in FIG. 43 shows that hotspot 0 occurs at x=1303, y=350 andis 190 pixels wide and 71 pixels high. The content of the hotspot isalso shown, i.e., http://www.ricoh.com.

According to an alternate embodiment of capture module 3820, a filter ina Microsoft XPS (XML print specification) print driver, commonly knownas an “XPSDrv filter,” receives text drawing commands and creates thepage_desc.xml file as described above.

Visibly Perceptible Hotspots

FIG. 45 illustrates a flowchart of a method of transforming characterscorresponding to a hotspot in a document in accordance with oneembodiment of the present invention. The method modifies printeddocuments in a way that indicates to both the end user and MMRrecognition software that a hot spot is present.

Initially, an electronic document to be printed is received 4510 as acharacter stream. For example, the document may be received 4510 at aprinter driver or at a software module capable of filtering thecharacter stream. In one embodiment, the document is received 4510 at abrowser 3715 from source files 3710. FIG. 46 illustrates an example ofan electronic version of a document 4610 according to one embodiment ofthe present invention. The document 4610 in this example has twohotspots, one associated with “are listed below” and one associated with“possible prior art.” The hotspots are not visibly perceptible by theend user according to one embodiment. The hotspots may be establishedvia the coordinate capture method described in reference to FIG. 38, oraccording to any of the other methods described herein.

The document is parsed 4520 for a begin mark, indicating the beginningof a hotspot. The begin mark may be a fiducial mark as previouslydescribed, or any other individually distinguishable mark thatidentifies a hotspot. Once a beginning mark is found, a transformationrule is applied 4530 to a portion of the document, i.e., the charactersfollowing the beginning mark, until an end mark is found. Thetransformation rule causes a visible modification of the portion of thedocument corresponding to the hotspot according to one embodiment, forexample by modifying the character font or color. In this example, theoriginal font, e.g., Times New Roman, may be converted to a differentknown font, e.g., OCR-A. In another example, the text is rendered in adifferent font color, e.g., blue #F86A. The process of transforming thefont is similar to the process described above according to oneembodiment. For example, if the document 4610 is an HTML file, when thefiducial marks are encountered in the document 4510 the font issubstituted in the HTML file.

According to one embodiment, the transformation step is accomplished bya plug-in 3720 to the browser 3715, yielding a modified document 3730.FIG. 47 illustrates an example of a printed modified document 4710according to one embodiment of the present invention. As illustrated,hotspots 4720 and 4730 are visually distinguishable from the remainingtext. In particular, hotspot 4720 is visually distinguishable based onits different font, and hotspot 4730 is visually distinguishable basedon its different color and underlining.

Next, the document with the transformed portion is rendered 4540 into apage layout, comprising the electronic document and the location of thehot spot within the electronic document. In one embodiment, renderingthe document is printing the document. In one embodiment, renderingincludes performing feature extraction on the document with thetransformed portion, according to any of the methods of so doingdescribed herein. In one embodiment, feature extraction includes, inresponse to a print command, capturing page coordinates corresponding tothe electronic document, according to one embodiment. The electronicdocument is then parsed for a subset of the coordinates corresponding tothe transformed characters. According to one embodiment, the capturemodule 3735 of FIG. 37A performs the feature extraction and/orcoordinate capture.

MMR recognition software preprocesses every image using the sametransformation rule. First it looks for text that obeys the rule, e.g.,it's in OCR-A or blue #F86A, and then it applies its normal recognitionalgorithm.

This aspect of the present invention is advantageous because it reducessubstantially the computational load of MMR recognition software becauseit uses a very simple image preprocessing routine that eliminates alarge amount of the computing overhead. In addition, it improves theaccuracy of feature extraction by eliminating the large number ofalternative solutions that might apply from selection, e.g., if abounding box over a portion of the document, e.g., as discussed inreference to FIGS. 51A-D. In addition, the visible modification of thetext indicates to the end user which text (or other document objects)are part of a hot spot.

Shared Document Annotation

FIG. 48 illustrates a flowchart of a method of shared documentannotation in accordance with one embodiment of the present invention.The method enables users to annotate documents in a shared environment.In the embodiment described below, the shared environment is a web pagebeing viewed by various users; however, the shared environment can beany environment in which resources are shared, such as a workgroup,according to other embodiments.

According to the method, a source document is displayed 4810 in abrowser, e.g., browser 3715. In one embodiment, the source document isreceived from source files 3710; in another embodiment, the sourcedocument is a web page received via a network, e.g., Internetconnection. Using the web page example, FIG. 49A illustrates a samplesource web page 4910 in a browser according to one embodiment of thepresent invention. In this example, the web page 4910 is an HTML filefor a game related to a popular children's book character, the JerryButter Game.

Upon display 4810 of the source document, a shared annotation and adesignation of a portion of the source document associated with theshared annotation associated with the source document are received 4820.A single annotation is used in this example for clarity of description,however multiple annotations are possible. In this example, theannotations are data or interactions used in MMR as discussed herein.The annotations are stored at, and received by retrieval from, a SharedDocumentation Annotation server (SDA server), e.g., 3755 as shown inFIG. 37A, according to one embodiment. The SDA server 3755 is accessiblevia a network connection in one embodiment. A plug-in for retrieval ofthe shared annotations facilitates this ability in this example, e.g.,plug-in 3720 as shown in FIG. 37A. According to another embodiment, theannotations and designations are received from a user. A user may createa shared annotation for a document that does not have any annotations,or may add to or modify existing shared annotations to a document. Forexample, the user may highlight a portion of the source document,designating it for association with a shared annotation, also providedby the user via various methods described herein.

Next, a modified document is displayed 4830 in the browser. The modifieddocument includes a hotspot corresponding to the portion of the sourcedocument designated in step 4820. The hotspot specifies the location forthe shared annotation. The modified document is part of the modifiedfiles 3730 created by plug-in 3720 and returned to browser 3715according to one embodiment. FIG. 49B illustrates a sample modified webpage 4920 in a browser according to one embodiment of the presentinvention. The web page 4920 shows a designation for a hotspot 4930 andthe associated annotation 4940, which is a video clip in this example.The designation 4930 may be visually distinguished from the remainingweb page 4920 text, e.g., by highlighting. According to one embodiment,the annotation 4940 displays when the designation 4930 is clicked on ormoused over.

In response to a print command, text coordinates corresponding to aprinted representation of the modified document and the hotspot arecaptured 4840. The details of coordinate capture are according to any ofthe methods for that purpose described herein.

Then, a page layout of the printed representation including the hot spotis rendered 4850. According to one embodiment, the rendering 4850 isprinting the document. FIG. 49C illustrates a sample printed web page4950 according to one embodiment of the present invention. The printedweb page layout 4950 includes the hotspot 4930 as designated, howeverthe line breaks in the print layout 4950 differ from the web page 4920.The hotspot 4930 boundaries are not visible on the printed layout 4950in this example.

In an optional final step, the shared annotations are stored locally,e.g., in data storage 3750, and are indexed using their associationswith the hotspots 4930 in the printed document 4950. The printedrepresentation also may be saved locally. In one embodiment, the act ofprinting triggers the downloading and creation of the local copy.

Hotspots for Imaged Documents

FIG. 50A illustrates a flowchart of a method of adding a hotspot to animaged document in accordance with one embodiment of the presentinvention. The method allows hotspots to be added to a paper documentafter it is scanned, or to a symbolic electronic document after it isrendered for printing.

First, a source document is converted 5010 to an imaged document. Thesource document is received at a browser 3715 from source files 3710according to one embodiment. The conversion 5010 is by any method thatproduces a document upon which a feature extraction can be performed, toproduce a feature representation. According to one embodiment, a paperdocument is scanned to become an imaged document. According to anotherembodiment, a renderable page proof for an electronic document isrendered using an appropriate application. For example, if therenderable page proof is in a PostScript format, Ghostscript is used.FIG. 51A illustrates an example of a user interface 5105 showing aportion of a newspaper page 5110 that has been scanned according to oneembodiment. A main window 5115 shows an enlarged portion of thenewspaper page 5110, and a thumbnail 5120 shows which portion of thepage is being displayed.

Next, feature extraction is applied 5020 to the imaged document tocreate a feature representation. Any of the various feature extractionmethods described herein may be used for this purpose. The featureextraction is performed by the capture module 3735 described inreference to FIG. 37A according to one embodiment. Then one or morehotspots 5125 is added 5030 to the imaged document. The hotspot may bepre-defined or may need to be defined according to various embodiments.If the hotspot is already defined, the definition includes a pagenumber, the coordinate location of the bounding box for the hot spot onthe page, and the electronic data or interaction attached to the hotspot. In one embodiment, the hotspot definition takes the form of ahotspot.xml file, as illustrated in FIG. 43.

If the hotspot is not defined, the end user may define the hotspot. FIG.50B illustrates a flowchart of a method of defining a hotspot foraddition to an imaged document in accordance with one embodiment of thepresent invention. First, a candidate hotspot is selected 5032. Forexample, in FIG. 51A, the end user has selected a portion of thedocument as a hotspot using a bounding box 5125. Next, for a givendatabase, it is determined in optional step 5034 whether the hotspot isunique. For example, there should be enough text in the surroundingn″×n″ patch to uniquely identify the hot spot. An example of a typicalvalue for n is 2. If the hotspot is not sufficiently unique for thedatabase, the end user is presented with options in one embodimentregarding how to deal with an ambiguity. For example, a user interfacemay provide alternatives such as selecting a larger area or acceptingthe ambiguity but adding a description of it to the database. Otherembodiments may use other methods of defining a hotspot.

Once the hotspot location is selected 5032, data or an interaction isdefined 5036 and attached to the hotspot. FIG. 51B illustrates a userinterface for defining the data or interaction to associate with aselected hotspot. For example, once the user has selected the boundingbox 5125, an edit box 5130 is displayed. Using associated buttons, theuser may cancel 5135 the operation, simply save 5140 the bounding box5125, or assign 5145 data or interactions to the hotspot. If the userselects to assign data or interactions to the hotspot, an assign box5150 is displayed, as shown in FIG. 51C. The assign box 5150 allows theend user to assign images 5155, various other media 5160, and web links5165 to the hotspot, which is identified by an ID number 5170. The userthen can select to save 5175 the hotspot definition. Although a singlehotspot has been described for simplicity, multiple hotspots arepossible. FIG. 51D illustrates a user interface for displaying hotspots5125 within a document. In one embodiment, different color boundingboxes correspond to different data and interaction types.

In an optional step, the imaged document, hot spot definition, and thefeature representation are stored 5040 together, e.g., in data store3750.

FIG. 52 illustrates a method 5200 of using an MMR document 500 and theMMR system 100 b in accordance with an embodiment of the presentinvention.

The method 5200 begins by acquiring 5210 a first document or arepresentation of the first document. Example methods of acquiring thefirst document include the following: (1) the first document is acquiredby capturing automatically, via PD capture module 318, the text layoutof a printed document within the operating system of MMR computer 112;(2) the first document is acquired by capturing automatically the textlayout of a printed document within printer driver 316 of MMR computer112; (3) the first document is acquired by scanning a paper document viaa standard document scanner device 127 that is connected to, forexample, MMR computer 112; and (4) the first document is acquired bytransferring, uploading or downloading, automatically or manually, afile that is a representation of the printed document to the MMRcomputer 112. While the acquiring step has been described as acquiringmost or all of the printed document, it should be understood that theacquiring step 5210 could be performed for only the smallest portion ofa printed document. Furthermore, while the method is described in termsof acquiring a single document, this step may be performed to acquire anumber of documents and create a library of first documents.

Once the acquiring step 5210 is performed, the method 5200 performs 5212an indexing operation on the first document. The indexing operationallows identification of the corresponding electronic representation ofthe document and associated second media types for input that matchesthe acquired first document or portions thereof. In one embodiment ofthis step, a document indexing operation is performed by the PD capturemodule 318 that generates the PD index 322. Example indexing operationsinclude the following: (1) the x-y locations of characters of a printeddocument are indexed; (2) the x-y locations of words of a printeddocument are indexed; (3) the x-y locations of an image or a portion ofan image in a printed document are indexed; (4) an OCR imaging operationis performed, and the x-y locations of characters and/or words areindexed accordingly; (4) feature extraction from the image of therendered page is performed, and the x-y locations of the features areindexed; and (5) the feature extraction on the symbolic version of apage are simulated, and the x-y locations of the features are indexed.The indexing operation 5212 may include any of the above or groups ofthe above indexing operations depending on application of the presentinvention.

The method 5200 also acquires 5214 a second document. In this step 5214,the second document acquired can be the entire document or just aportion (patch) of the second document. Example methods of acquiring thesecond document include the following: (1) scanning a patch of text, bymeans of one or more capture mechanisms 230 of capture device 106; (2)scanning a patch of text by means of one or more capture mechanisms 230of capture device 106 and, subsequently, preprocessing the image todetermine the likelihood that the intended feature description will beextracted correctly. For example, if the index is based on OCR, thesystem might determine whether the image contains lines of text andwhether the image sharpness is sufficient for a successful OCRoperation. If this determination fails, another patch of text isscanned; (3) scanning a machine-readable identifier (e.g., internationalstandard book number (ISBN) or universal produce code (UPC) code) thatidentifies the document that is scanned; (4) inputting data thatidentifies a document or a set of documents (e.g., 2003 editions ofSports Illustrated magazine) that is requested and, subsequently, apatch of text is scanned by use of items (1) or (2) of this method step;(5) receiving email with a second document attached; (6) receiving asecond document by file transfer; (7) scanning a portion of an imagewith one or more capture mechanisms 230 of capture device 106; and (9)inputting the second document with an input device 166.

Once the steps 5210 and 5214 have been performed, the method performs5216 document or pattern matching between the first document and thesecond document. In one embodiment, this is done by performing documentfingerprint matching of the second document to the first document. Adocument fingerprint matching operation is performed on the second mediadocument by querying PD index 322. An example of document fingerprintmatching is extracting features from the image captured in step 5214,composing descriptors from those features, and looking up the documentand patch that contains a percentage of those descriptors. It should beunderstood that this pattern matching step may be performed a pluralityof times, once for each document where the database stores numerousdocuments to determine if any documents in a library or database matchthe second document. Alternatively, the indexing step 5212 adds thedocument 5210 to an index that represents a collection of documents andthe pattern matching step is performed once.

Finally, the method 5200 executes 5218 an action based on result of step5216 and on optionally based on user input. In one embodiment, themethod 5200 looks up a predetermined action that is associated with thegiven document patch, as for example, stored in the second media 504associated with the hotspot 506 found as matching in step 5216. Examplesof predetermined actions include: (1) retrieving information from thedocument event database 320, the Internet, or elsewhere; (2) writinginformation to a location verified by the MMR system 100 b that is readyto receive the system's output; (3) looking up information; (4)displaying information on a client device, such as capture device 106,and conducting an interactive dialog with a user; (5) queuing up theaction and the data that is determined in method step 5216, for laterexecution (the user's participation may be optional); and (6) executingimmediately the action and the data that is determined in method step5216. Example results of this method step include the retrieval ofinformation, a modified document, the execution of some other action(e.g., purchase of stock or of a product), or the input of a commandsent to a cable TV box, such as set-top box 126, that is linked to thecable TV server (e.g., service provider server 122), which streams videoback to the cable TV box. Once step 5218 has been done, the method 5200is complete and ends.

FIG. 53 illustrates a block diagram of an example set of businessentities 5300 that are associated with MMR system 100 b, in accordancewith an embodiment of the present invention. The set of businessentities 5300 comprise an MMR service provider 5310, an MMR consumer5312, a multimedia company 5314, a printer user 5316, a cell phoneservice provider 5318, a hardware manufacturer 5320, a hardware retailer5322, a financial institution 5324, a credit card processor 5326, adocument publisher 5328, a document printer 5330, a fulfillment house5332, a cable TV provider 5334, a service provider 5336, a softwareprovider 5338, an advertising company 5340, and a business network 5370.

MMR service provider 5310 is the owner and/or administrator of an MMRsystem 100 as described with reference to FIGS. 1A through 5 and 52. MMRconsumer 5312 is representative of any MMR user 110, as previouslydescribed with reference to FIG. 1B.

Multimedia company 5314 is any provider of digital multimedia products,such as Blockbuster Inc. (Dallas, Tex.), that provides digital moviesand video games and Sony Corporation of America (New York, N.Y.) thatprovides digital music, movies, and TV shows.

Printer user 5316 is any individual or entity that utilizes any printerof any kind in order to produce a printed paper document. For example,MMR consumer 5312 may be printer user 5316 or document printer 5330.

Cell phone service provider 5318 is any cell phone service provider,such as Verizon Wireless (Bedminster, N.J.), Cingular Wireless (Atlanta,Ga.), T-Mobile USA (Bellevue, Wash.), and Sprint Nextel (Reston, Va.).

Hardware manufacturer 5320 is any manufacturer of hardware devices, suchas manufacturers of printers, cellular phones, or PDAs. Example hardwaremanufacturers include Hewlett-Packard (Houston, Tex.), Motorola, Inc,(Schaumburg, Ill.), and Sony Corporation of America (New York, N.Y.).Hardware retailer 5322 is any retailer of hardware devices, such asretailers of printers, cellular phones, or PDAs. Example hardwareretailers include, but are not limited to, RadioShack Corporation (FortWorth, Tex.), Circuit City Stores, Inc. (Richmond, Va.), Wal-Mart(Bentonville, Ark.), and Best Buy Co. (Richfield, Minn.).

Financial institution 5324 is any financial institution, such as anybank or credit union, for handling bank accounts and the transfer offunds to and from other banking or financial institutions. Credit cardprocessor 5326 is any credit card institution that manages the creditcard authentication and approval process for a purchase transaction.Example credit card processors include, but are not limited to,ClickBank, which is a service of Click Sales Inc, (Boise Id.), ShareIt!Inc. (Eden Prairie, Minn.), and CCNow Inc. (Eden Prairie, Minn.).

Document publisher 5328 is any document publishing company, such as, butnot limited to, The Gregath Publishing Company (Wyandotte, Okla.),Prentice Hall (Upper Saddle River, N.J.), and Pelican Publishing Company(Gretna, La.). Document printer 5330 is any document printing company,such as, but not limited to, PSPrint LLC (Oakland Calif.), PrintLizard,Inc., (Buffalo, N.Y.), and Mimeo, Inc. (New York, N.Y.). In anotherexample, document publisher 5328 and/or document printer 5330 is anyentity that produces and distributes newspapers or magazines.

Fulfillment house 5332 is any third-party logistics warehouse thatspecializes in the fulfillment of orders, as is well known. Examplefulfillment houses include, but are not limited to, Corporate DiskCompany (McHenry, Ill.), OrderMotion, Inc. (New York, N.Y.), andShipwire.com (Los Angeles, Calif.).

Cable TV provider 5334 is any cable TV service provider, such as, butnot limited to, Comcast Corporation (Philadelphia, Pa.) and AdelphiaCommunications (Greenwood Village, Colo.). Service provider 5336 isrepresentative of any entity that provides a service of any kind.

Software provider 5338 is any software development company, such as, butnot limited to, Art & Logic, Inc. (Pasadena, Calif.), Jigsaw Data Corp.(San Mateo, Calif.), DataMirror Corporation (New York, N.Y.), andDataBank IMX, LCC (Beltsville, Md.).

Advertising company 5340 is any advertising company or agency, such as,but not limited to, D and B Marketing (Elhurst, Ill.), BlackSheepMarketing (Boston, Mass.), and Gotham Direct, Inc. (New York, N.Y.).

Business network 5370 is representative of any mechanism by which abusiness relationship is established and/or facilitated.

FIG. 54 illustrates a method 5400, which is a generalized businessmethod that is facilitated by use of MMR system 100 b, in accordancewith an embodiment of the present invention. Method 5400 includes thesteps of: establishing relationship between at least two entities,determining possible business transactions; executing at least onebusiness transaction and delivering product or service for thetransaction.

First, a relationship is established 5410 between at least two businessentities 5300. The business entities 5300 may be aligned within, forexample, four broad categories, such as (1) MMR creators, (2) MMRdistributors, (3) MMR users, and (4) others, and within which somebusiness entities fall into more than one category. According to thisexample, business entities 5300 are categorized as follows:

-   MMR creators—MMR service provider 5310, multimedia company 5314,    document publisher 5328, document printer 5330, software provider    5338 and advertising company 5340;-   MMR distributors—MMR service provider 5310, multimedia company 5314,    cell phone service provider 5318, hardware manufacturer 5320,    hardware retailer 5322, document publisher 5328, document printer    5330, fulfillment house 5332, cable TV provider 5334, service    provider 5336 and advertising company 5340;-   MMR users—MMR consumer 5312, printer user 5316 and document printer    5330; and-   Others—financial institution 5324 and credit card processor 5326.

For example in this method step, a business relationship is establishedbetween MMR service provider 5310, which is an MMR creator, and MMRconsumer 5312, which is an MMR user, and cell phone service provider5318 and hardware retailer 5322, which are MMR distributors.Furthermore, hardware manufacturer 5320 has a business relationship withhardware retailer 5322, both of which are MMR distributors.

Next, the method 5400 determines 5412 possible business transactionsbetween the parties with relationships established in step 5410. Inparticular, a variety of transactions may occur between any two or morebusiness entities 5300. Example transactions include: purchasinginformation; purchasing physical merchandise; purchasing services;purchasing bandwidth; purchasing electronic storage; purchasingadvertisements; purchasing advertisement statistics; shippingmerchandise; selling information; selling physical merchandise; sellingservices, selling bandwidth; selling electronic storage; sellingadvertisements; selling advertisement statistics; renting/leasing; andcollecting opinions/ratings/voting.

Once the method 5400 has determined possible business transactionsbetween the parties, the MMR system 100 is used to reach 5414 agreementon at least one business transaction. In particular, a variety ofactions may occur between any two or more business entities 5300 thatare the result of a transaction. Example actions include: purchasinginformation; receiving an order; clicking-through, for more information;creating ad space; providing local/remote access; hosting; shipping;creating business relationships; storing private information;passing-through information to others; adding content; and podcasting.

Once the method 5400 has reached agreement on the business transaction,the MMR system 100 is used to deliver 5416 products or services for thetransaction, for example, to the MMR consumer 5312. In particular, avariety of content may be exchanged between any two or more businessentities 5300, as a result of the business transaction agreed to inmethod step 5414. Example content includes: text; web link; software;still photos; video; audio; and any combination of the above.Additionally, a variety of delivery mechanisms may be utilized betweenany two or more business entities 5300, in order to facilitate thetransaction. Example delivery mechanisms include: paper; personalcomputer; networked computer; capture device 106; personal video device;personal audio device; and any combination of the above.

The algorithms presented herein are not inherently related to anyparticular computer or other apparatus. Various general-purpose and/orspecial purpose systems may be programmed or otherwise configured inaccordance with embodiments of the present invention. Numerousprogramming languages and/or structures can be used to implement avariety of such systems, as will be apparent in light of thisdisclosure. Moreover, embodiments of the present invention can operateon or work in conjunction with an information system or network. Forexample, the invention can operate on a stand alone multifunctionprinter or a networked printer with functionality varying depending onthe configuration. The present invention is capable of operating withany information system from those with minimal functionality to thoseproviding all the functionality disclosed herein.

The foregoing description of the embodiments of the present inventionhas been presented for the purposes of illustration and description. Itis not intended to be exhaustive or to limit the present invention tothe precise form disclosed. Many modifications and variations arepossible in light of the above teaching. It is intended that the scopeof the present invention be limited not by this detailed description,but rather by the claims of this application. As will be understood bythose familiar with the art, the present invention may be embodied inother specific forms without departing from the spirit or essentialcharacteristics thereof. Likewise, the particular naming and division ofthe modules, routines, features, attributes, methodologies and otheraspects are not mandatory or significant, and the mechanisms thatimplement the present invention or its features may have differentnames, divisions and/or formats. Furthermore, as will be apparent to oneof ordinary skill in the relevant art, the modules, routines, features,attributes, methodologies and other aspects of the present invention canbe implemented as software, hardware, firmware or any combination of thethree. Also, wherever a component, an example of which is a module, ofthe present invention is implemented as software, the component can beimplemented as a standalone program, as part of a larger program, as aplurality of separate programs, as a statically or dynamically linkedlibrary, as a kernel loadable module, as a device driver, and/or inevery and any other way known now or in the future to those of ordinaryskill in the art of computer programming. Additionally, the presentinvention is in no way limited to implementation in any specificprogramming language, or for any specific operating system orenvironment. Accordingly, the disclosure of the present invention isintended to be illustrative, but not limiting, of the scope of thepresent invention, which is set forth in the following claims.

1. A computer implemented method for organizing and accessinginformation in a mixed media document system, comprising: generating anelectronic representation of the paper document; identifying features onthe paper document that capture two-dimensional aspects of the paperdocument; identifying locations of the features; and indexing thefeatures by their respective locations, thereby generating an indextable.
 2. The method of claim 1 further comprising the preliminary stepof receiving the paper document.
 3. The method of claim 1 furthercomprising: storing one or more characteristics associated with at leastone of the features.
 4. The method of claim 3 wherein the one or morecharacteristics include one or more actions including at least one ofretrieval of textual information, retrieval of graphical information,executing a process, executing a command, placing an order, retrieving avideo, retrieving a sound, storing information, creating a new document,printing a document, and displaying a document.
 5. The method of claim 1wherein identifying features on the paper document that capturetwo-dimensional aspects includes identifying horizontally aligned andvertically aligned objects.
 6. The method of claim 1 wherein identifyingfeatures on the paper document that capture two-dimensional aspectsincludes identifying horizontal and vertical word-pairs.
 7. The methodof claim 1 wherein generating an electronic representation of the paperdocument is carried out during a scanning or printing process.
 8. Themethod of claim 1 wherein identifying features on the paper documentthat capture two-dimensional aspects of the paper document includesgrouping sequences of text into logical lines by checking amount ofvertical overlap between two consecutive sequences.
 9. The method ofclaim 1 further comprising: receiving one or more query terms thatcapture two-dimensional relationships between objects in a targetdocument; and computing at least one mixed media document and locationhypotheses potentially responsive to the query terms, based on data fromthe index table.
 10. The method of claim 9 wherein receiving one or morequery terms that capture two-dimensional relationships between objectsin a target document is preceded by: receiving the target document;creating an image of at least a patch of target document; and generatingthe one or more query terms based on the image.
 11. The method of claim10 wherein generating the one or more query terms based on the imageincludes generating horizontal and vertical word-pairs extracted fromthe image.
 12. The method of claim 11 wherein computing at least onemixed media document and location hypotheses includes: locating a storedpage that most likely matches the patch of the target document; andcalculating a location within that page that is most likely the centerof the patch.
 13. The method of claim 12 wherein each word-pair isassociated with an inverse document frequency, and locating a storedpage that most likely matches at least a patch of the target documentincludes: adding the inverse document frequency for each word-pair to anaccumulator indexed by documents pages on which that word-pair appears;and in response to a maximum value in that accumulator exceeding athreshold, outputting the corresponding document page as a match to thepatch.
 14. The method of claim 13 wherein calculating a location withinthat page that is most likely the center of the patch includes: adding aweight to each cell in a zone around each word-pair, wherein the weightfor each cell is determined by product of the inverse document frequencyof that word-pair and a normalized geometric distance between that celland the center of the zone; searching a corresponding Accum array of theaccumulator for the cell with a maximum value; and in response to themaximum value exceeding a threshold, reporting coordinates of the cellas the location of the patch.
 15. The method of claim 9 whereincomputing at least one mixed media document and location hypothesesincludes: looking-up each of the one or more query terms in the indextable to retrieve one or more locations associated with each query term;and for each identified location, identifying one or more candidateregions that contain the location.
 16. The method of claim 15 whereincomputing at least one mixed media document and location hypothesesincludes: identifying one of the one or more candidate regions that ismost consistent with all of the one or more query terms; and in responseto determining that the one of the one or more candidate regionssatisfies a pre-defined matching criteria, confirming the region as amatch to the target document.
 17. A machine-readable medium encoded withinstructions, that when executed by one or more processors, cause theprocessor to carry out a process for organizing and accessinginformation in a mixed media document system, the process comprising:generating an electronic representation of the paper document;identifying features on the paper document that capture two-dimensionalaspects of the paper document; identifying locations of the features;and indexing the features by their respective locations, therebygenerating an index table.
 18. The machine-readable medium of claim 17,the process further comprising: storing one or more characteristicsassociated with at least one of the features.
 19. The machine-readablemedium of claim 17 wherein identifying features on the paper documentthat capture two-dimensional aspects includes identifying horizontal andvertical word-pairs.
 20. The machine-readable medium of claim 17, theprocess further comprising: receiving one or more query terms thatcapture two-dimensional relationships between objects in a targetdocument; and computing at least one mixed media document and locationhypotheses potentially responsive to the query terms, based on data fromthe index table.
 21. A computer implemented method for accessinginformation in a mixed media document system, comprising: receiving oneor more query terms that capture two-dimensional relationships betweenobjects in a target document; and computing at least one mixed mediadocument and location hypotheses potentially responsive to the queryterms, based on data from an index table that indexes document featuresand feature locations of mixed media documents.
 22. The method of claim21 wherein receiving one or more query terms that capturetwo-dimensional relationships between objects in a target document ispreceded by: receiving the target document; creating an image of atleast a patch of target document; and generating the one or more queryterms based on the image.
 23. The method of claim 22 wherein generatingthe one or more query terms based on the image includes generatinghorizontal and vertical word-pairs extracted from the image.
 24. Themethod of claim 23 wherein computing at least one mixed media documentand location hypotheses includes: locating a stored page that mostlikely matches the patch of the target document; and calculating alocation within that page that is most likely the center of the patch.25. The method of claim 24 wherein each word-pair is associated with aninverse document frequency, and locating a stored page that most likelymatches at least a patch of the target document includes: adding theinverse document frequency for each word-pair to an accumulator indexedby documents pages on which that word-pair appears; and in response to amaximum value in that accumulator exceeding a threshold, outputting thecorresponding document page as a match to the patch.
 26. The method ofclaim 25 wherein calculating a location within that page that is mostlikely the center of the patch includes: adding a weight to each cell ina zone around each word-pair, wherein the weight for each cell isdetermined by product of the inverse document frequency of thatword-pair and a normalized geometric distance between that cell and thecenter of the zone; searching a corresponding Accum array of theaccumulator for the cell with a maximum value; and in response to themaximum value exceeding a threshold, reporting coordinates of the cellas the location of the patch.
 27. The method of claim 21 whereincomputing at least one mixed media document and location hypothesesincludes: looking-up each of the one or more query terms in the indextable to retrieve one or more locations associated with each query term;and for each identified location, identifying one or more candidateregions that contain the location.
 28. The method of claim 27 whereincomputing at least one mixed media document and location hypothesesincludes: identifying one of the one or more candidate regions that ismost consistent with all of the one or more query terms; and in responseto determining that the one of the one or more candidate regionssatisfies a pre-defined matching criteria, confirming the region as amatch to the target document.
 29. A machine-readable medium encoded withinstructions, that when executed by one or more processors, cause theprocessor to carry out a process for accessing information in a mixedmedia document system, the process comprising: receiving one or morequery terms that capture two-dimensional relationships between objectsin a target document; and computing at least one mixed media documentand location hypotheses potentially responsive to the query terms, basedon data from an index table that indexes document features and featurelocations of mixed media documents.
 30. The machine-readable medium ofclaim 29 wherein receiving one or more query terms that capturetwo-dimensional relationships between objects in a target document ispreceded by: receiving the target document; creating an image of atleast a patch of target document; and generating the one or more queryterms based on the image.
 31. The machine-readable medium of claim 30wherein generating the one or more query terms based on the imageincludes generating horizontal and vertical word-pairs extracted fromthe image.
 32. The machine-readable medium of claim 31 wherein computingat least one mixed media document and location hypotheses includes:locating a stored page that most likely matches the patch of the targetdocument; and calculating a location within that page that is mostlikely the center of the patch.
 33. The machine-readable medium of claim32 wherein each word-pair is associated with an inverse documentfrequency, and locating a stored page that most likely matches at leasta patch of the target document includes: adding the inverse documentfrequency for each word-pair to an accumulator indexed by documentspages on which that word-pair appears; and in response to a maximumvalue in that accumulator exceeding a threshold, outputting thecorresponding document page as a match to the patch.
 34. Themachine-readable medium of claim 33 wherein calculating a locationwithin that page that is most likely the center of the patch includes:adding a weight to each cell in a zone around each word-pair, whereinthe weight for each cell is determined by product of the inversedocument frequency of that word-pair and a normalized geometric distancebetween that cell and the center of the zone; searching a correspondingAccum array of the accumulator for the cell with a maximum value; and inresponse to the maximum value exceeding a threshold, reportingcoordinates of the cell as the location of the patch.
 35. Themachine-readable medium of claim 29 wherein computing at least one mixedmedia document and location hypotheses includes: looking-up each of theone or more query terms in the index table to retrieve one or morelocations associated with each query term; for each identified location,identifying one or more candidate regions that contain the location;identifying one of the one or more candidate regions that is mostconsistent with all of the one or more query terms; and in response todetermining that the one of the one or more candidate regions satisfiesa pre-defined matching criteria, confirming the region as a match to thetarget document.